Tsukasa Iwashina
email: iwashina@kahaku.go.jp

Hiroshi Tobe

Takahisa Nakane

Takayuki Mizuno

Tanguy Jaffré

Flavonoids and Phenolic Compounds From the Parasitic Gymnosperm Parasitaxus usta Endemic to New Caledonia

Keywords: flavonoids, phenolic compounds, Parasitaxus usta, Podocarpaceae, parasitic gymnosperm

), and Z-caffeic acid methyl ester 3-O-β-glucopyranoside (17) by UV, LC-MS and NMR. Chemical components of P usta were reported in this survey for the first time. Their chemical characters were chemotaxonomically compared with those of other Podocarpaceae species.

Parasitaxus usta (Vieill.) de Laub. is the only parasitic gymnosperm in the world, and endemic to New Caledonia. It is a woody shrub or small tree, up to 1.5 m in height with deep wine red to purple scale leaves. The plant lacks roots and is always found attached to the roots of Falcatifolium taxoides (Brongn. & Gris) de Laub., which is also a member of Podocarpaceae [START_REF] Laubenfels | Parasitic conifer found in New Caledonia[END_REF][START_REF] Laubenfels | Flora de la Nouvelle Caledonia et Dependances[END_REF] . Although some Podocarpaceae such as Dacrycarpus 3 , Podocarpus 4-6 and Phyllocladus [START_REF] Foo | Phenylpropanoid derivatives of catechin, epicatechin and phylloflavan from Phyllocladus trichomanoides[END_REF][START_REF] Foo | Flavonocoumarins and flavanophenylpropanoids from Phyllocladus trichomanoides[END_REF][START_REF] Markham | Uniformity and distinctness of Phyllocladus as evidenced by flavonoid accumulation[END_REF] have been reported for flavonoids, chemical compounds including flavonoids in Parasitaxus are not surveyed as far as we know. In this survey, flavonoids and related phenolic compounds were isolated and identified from P. usta for the first time.

Results

Eleven flavonoids (1-11) were isolated from P. usta. Compound 9 is anthocyanin and was identified as cyanidin 3-O-glucoside (chrysanthemin, Figure 1) by UV-vis spectra, liquid chromatograph-mass spectra (LC-MS), acid hydrolysis, and HPLC comparison with authentic sample from Acer spp. leaves [START_REF] Hattori | Studien über anthocyane II. Über die farbstoffe aus roten herbstlättern von einigen Acer arten[END_REF] . Flavonoids 2 and 4 were flavone aglycones and identified as luteolin and chrysoeriol (Figure 1) by UV, LC-MS, and HPLC comparisons with authentic samples from the pubescence of Glycine max (L.) Merr. [START_REF] Iwashina | Analysis of flavonoids in pubescence of soybean near-isogenic lines for pubescence color loci[END_REF] and Extrasynthese (Genay). Of flavonoids 1, 3 and 8, the former two were flavone glycosides. On the other hand, 8 was flavonol glycoside which was shown by UV spectral survey. Finally, 1, 3 and 8 were identified as apigenin 7-O-glucoside, luteolin 7-O-glucoside, and quercetin 3-O-glucoside (isoquercitrin) (Figure 1) by LC-MS, acid hydrolysis, and HPLC comparisons with authentic samples from Extrasynthese, the leaves of Schmalhausenia nidulans Petrak (Asteraceae) [START_REF] Iwashina | Flavonoids from Schumalhausenia nidulans (Compositae): a taxon endemic to the Tien Sian mountains[END_REF] , and the fronds of Cyrtomium spp. (Dryopteridaceae) [START_REF] Iwashina | Flavonoids in the species of Cyrtomium (Dryopteridaceae) and related genera[END_REF] , respectively. Flavonoid 7 was unhydrolyzable, showing that the compound is C-glycosylflavone. Finally, 7 was identified as orientin (Figure 1) by HPLC and TLC comparison with authentic sample from the leaves of Barringtonia asiatica (L.) Kurz. (Lecythidaceae) [START_REF] Iwashina | Flavonoid properties in the leaves of Barringtonia asiatica (Lecythidaceae)[END_REF] . Flavonoids 5 and 6 were flavone glycosides and characterized as chrysoeriol 7-O-glucoside and tricetin 3 ′ -O-glucoside (Figure 1) by UV spectral survey according to Mabry et al. [START_REF] Mabry | The Systematic Identification of Flavonoids[END_REF] , LC-MS and acid hydrolysis. 1). Six phenolic compounds (12-17) were isolated from P. usta. The molecular ion peaks of 12 and 13 showed m/z 339 [M + H] + and 337 [M-H] -, showing the attachment of 1 mol p-coumaric acid to quinic acid. In 1 H NMR, four proton signals corresponding to H-2,6, H-3,5, H-α, and H-β of p-coumaric acid occurred. Moreover, four proton signals corresponding to H-2,6, H-3, H-4, and H-5 of quinic acid were recognized. In HMBC, H-5 proton signal of quinic acid correlated with COOH carbon signal of p-coumaric acid. Although proton and carbon signals of 12 and 13 were essentially the same, the coupling constants of H-α and H-β of p-coumaric acid were different, i.e. J = 15.6 Hz and 15.6 Hz in 12, and J = 13.2 Hz and 13.2 Hz in 13, showing that 12 and 13 are Eand Z-forms, respectively. Thus, 12 and 13 were identified as 5-O-E-p-coumaroyl quinic acid and 5-O-Z-p-coumaroyl quinic acid (Figure 1), respectively. 1 H and 13 C NMR data of 14 and 15 were essentially the same with those of 12 and 13, except for the presence of methoxyl proton and carbon signals. In HMBC, since methoxyl proton signal correlated with COOH carbon signals of quinic acid, it was shown that methoxyl group is attached to the carboxyl group of quinic acid. Thus, 14 and 15 were identified as 5-O-E-p-coumaroyl quinic acid methyl ester and 5-O-Z-p-coumaroyl quinic acid methyl ester (Figure 1), respectively. The molecular ion peaks of 16 and 17 showed m/z 357 [M + H] + and 355 [M -H] -, showing the attachment of 1 mol hexose to dihydroxy-monomethoxy-cinnamic acid. In 1 H NMR, five proton signals corresponding to H-2, H-5, H-6, H-α, and H-β of caffeic acid occurred, together with a methoxyl proton signal. In HMBC, anomeric proton signal correlated with the C-3 carbon signal of caffeic acid. On the other hand, a methoxyl proton signal correlated with COOH carbon signal of caffeic acid. Moreover, coupling constants of H-α and H-β of 16 were J = 15.6 Hz, showing the caffeic acid is E-form. Thus, 16 was identified as E-caffeic acid methyl ester 3-O-β-glucopyranoside (Figure 1). On the other hand, since coupling constants of H-α and H-β of 17 were J = 12.6 Hz, caffeic acid is Z-form. From the results described above, 17 was identified as Z-caffeic acid methyl ester 3-O-β-glucopyranoside (Figure 1).

Discussion

In this survey, six flavones (1-6), one C-glycosylflavone (7), one flavonol (8), one anthocyanin (9), two biflavones (10 and 11), and six phenolic compounds (12-17) were isolated from P. usta for the first time. Many flavonoids have been reported from Podocarpaceae species such as Dacrycarpus 3 , Podocarpus [START_REF] Markham | Support from flavonoid glycoside distribution for the division of Podocarpus in New Zealand[END_REF][START_REF] Miura | Studies on bisflavones in the leaves of Podocarpus macrophylla and P. nagi[END_REF] and Phyllocladus 9 . Of the flavonoids which were isolated in this survey, biflavones were widespread reported in gymnosperms [START_REF] Kariyone | Studies on the components of plants, belonging to coniferae and allied orders; especially on the wax and biflavones of the leaves[END_REF] . However, isoginkgetin (10) and agathisflavone (11) are not found in Podocarpaceae as far as we know. Compounds 1, 3, 7 to 9 are common flavonoids and have been reported from some Podocarpaceae species, e.g. 1 and 3 from Prumnopitys spp. [START_REF] Markham | Support from flavonoid glycoside distribution for the division of Podocarpus in New Zealand[END_REF] , 7 to 9 from Podocarpus spp. [START_REF] Andersen | Delphinidin-3-neohesperidoside and cyanidin-3neohesperidoside from receptacles of Podocarpus species[END_REF][START_REF] Markham | Support from flavonoid glycoside distribution for the division of Podocarpus in New Zealand[END_REF] , Dacrycarpus dacrydioides [START_REF] Andersen | Semipreparative isolation and structure determination of pelargonidin 3-O-α-L-rhamnopyranosyl-(1→2)-β-D-glucopyranoside and other anthocyanin from the tree Dacrycarpus dacrydioides[END_REF][START_REF] Markham | Support from flavonoid glycoside distribution for the division of Podocarpus in New Zealand[END_REF] , and Prumnopitys spp. [START_REF] Markham | Support from flavonoid glycoside distribution for the division of Podocarpus in New Zealand[END_REF] . Although 6 is a comparatively rare flavonoid, it has been found in Podocarpus totara [START_REF] Markham | Support from flavonoid glycoside distribution for the division of Podocarpus in New Zealand[END_REF] . Flavonoids 2, 4, and 5 are also common compounds. However, they were reported from Podocarpaceous species for the first time. Six phenolic compounds (12-17) which were isolated in this survey are not reported from the Podocarpaceous plants.

It has been shown that P. usta correlated with the genera Lagarostrobus and Manoao by chloroplast trnL-F intron/spacer and nuclear rDNA ITS2 sequences [START_REF] Sinclair | Evolutionary relationships of the New Caledonian heterotrophic conifer, Parasitaxus usta (Podocarpaceae), inferred from chloroplast trnL-F intron/ spacer and nuclear rDNA ITS2 sequences[END_REF] .On the other hand, it was shown by the single-copy nuclear gene that Parasitaxus correlates with Phyllocladus tricomanoides [START_REF] Lu | Phylogeny and divergence times of gymnosperms inferred from single-copy nuclear genes[END_REF] . Although the flavonoids of Lagarostrobus and Manoao species are not reported as far as we know, those of P. trichomanoides have been isolated and some catechins such as ( + )-catechin and (-)-epicatechin have been identified [START_REF] Foo | Phenylpropanoid derivatives of catechin, epicatechin and phylloflavan from Phyllocladus trichomanoides[END_REF][START_REF] Foo | Flavonocoumarins and flavanophenylpropanoids from Phyllocladus trichomanoides[END_REF][START_REF] Polya | Inhibition of eukaryote signal-regulated protein kinase by plant-derived catechin-related compounds[END_REF] . However, catechins were apparently not found in Parasitaxus in this survey. As a result, the chemical composition of P. usta have the chemical characters of Podocarpaceae species, e.g. the presence of common flavones, flavonol, C-glycosylflavone, anthocyanin, and especially biflavones. However, two biflavones, isoginkgetin (10) and agathisflavone (11), and phenolic compounds 12 to 17 are not reported from the family as far as we know. Thus, it was suggested that the chemical characters of Parasitaxus is chemotaxonomically unique in Podocarpaceae.

Materials and Methods

Plant Materials

P. usta was collected in Mt. Dzumac (22°01 ′ 50.94 ′′ S, 166°2 8 ′ 03.42 ′′ E, 900 m alt.), New Caledonia in January 11, 1998 by the authors (H. Tobe and T. Jaffré), and deposited in the herbarium of New Caledonian Herbarium at Nouméa (NOU).

General

Analytical HPLC was performed with Shimadzu HPLC systems using Inertsil ODS-4 column (I.D. 6.0 × 150 mm, GL Sciences, Tokyo) at a flow rate of 1.0 mL/min. Detection wavelength was 350 (flavonoids and phenolic compounds) and 530 nm (anthocyanin). Eluent was MeCN/H 2 O/H 3 PO 4 = 20:80:0.2). LC-MS was performed on a Shimadzu HPLC/UV-vis/ESI-MS system using Inertsil ODS-4 column (I.D. 2.1 × 100 mm), flow rate of 0.2 mL/min, detection wavelength of 350 and 530 nm, electrospray ionization (ESI + ) 4.5 kV and ESI -3.5 kV, 25°C, and elution with MeCN/H 2 O/HCOOH (20:75:5 for flavonoids except for biflavones and phenolic compounds or 65:35:5 for biflavones). NMR spectra ( 1 H and [START_REF] Iwashina | Flavonoids in the species of Cyrtomium (Dryopteridaceae) and related genera[END_REF] C NMR, 1 H-1 H correlation spectroscopy, 1 H-1 H total COSY, heteronuclear quantum correlation, and HMBC) were recorded on a Bruker AV-600 in DMSO-d 6 at 600 MHz ( 1 H NMR) and 150 MHz ( 13 C NMR). Preparative paper chromatography (prep. PC) was performed with solvent systems, BAW (n-BuOH/HOAc/H 2 O = 4:1:5, upper phase), BEW (n-BuOH/EtOH/H 2 O = 4:1:2.2), and then 15%HOAc. Acid hydrolysis was performed in 12% hydrochloric acid at a temperature of 100°C for 30 min. After shaking with diethyl ether, flavonoid aglycones were migrated to the organic layer, and sugars and C-glycosylflavone were left in the aqueous layer. Preparative HPLC was performed with Shimadzu HPLC systems using Inertsil ODS-4 column (I.D. 10 × 250 mm), at a flow rate of 3.0 mL/min, detection wavelength of 350 nm, and the elution with MeCN/H 2 O/ HCOOH (20:75:5 for phenolic compounds or 50:45:6 for biflavones).

Extraction and Isolation

Dried aerial parts (25.0 g) of P. usta were extracted with MeOH/ HCOOH (92:8). The concentrated extracts were applied to prep. PC. Isolated flavonoid glycosides 1, 3, 5 to 9 were purified by Sephadex LH-20 column chromatography using solvent systems, 70% MeOH (1, 3, 5 to 8) and MeOH/H 2 O/ HCOOH (70:25:5) (9). Other compounds were applied to preparative HPLC. Compounds 3 (3.2 mg), 10 (1.2 mg), 11 (1.3 mg), 12 (1.7 mg), 13 (1.7 mg), 14 (14.8 mg), 15 (3.6 mg), and 16 (2.6 mg) were obtained as pale yellow or white powders.

Identification of the Compounds

Flavonoids and phenolic compounds were identified by UV-vis spectral survey according to Mabry et al. [START_REF] Mabry | The Systematic Identification of Flavonoids[END_REF] , LC-MS, characterization of acid hydrolysates, 1 H and [START_REF] Iwashina | Flavonoids in the species of Cyrtomium (Dryopteridaceae) and related genera[END_REF] C NMR, and/or HPLC comparisons with authentic samples. UV, LC-MS and NMR data are shown in online Supplemental Material.

  Flavonoids 10 and 11 were presumed as biflavones by the behavior of HPLC. The molecular ion peaks of 10 showed m/z 567 [M + H] + and 565 [M-H] -, suggesting that it is tetrahydroxy-dimethoxybiflavone. In NMR, the proton and carbon signals were assigned by COSY, NOESY, HSQC, and HMBC. The 1 H NMR spectrum of 10 showed 10 aromatic proton signals, H-6 ′ , H-2 ′ , H-2 ′′′ ,6 ′′′ , H-5 ′ , H-3, H-8, H-3 ′′ , H-3 ′′′ ,5 ′′′ , H-6 ′′ , and H-6, together with 2 methoxyl proton signals. Of their proton signals, two

Figure 1 .

 1 Figure 1. Chemical structures of flavonoids and phenolic compounds isolated from Parasitaxus usta.(continued)

Figure 1 .

 1 Figure 1. Continued.

  The molecular ion peaks of 11 showed m/z 539 [M + H] + and 537 [M-H] -, showing it is hexahydroxybiflavone. In 1 H and[START_REF] Iwashina | Flavonoids in the species of Cyrtomium (Dryopteridaceae) and related genera[END_REF] C NMR, the proton and carbon signals were assigned by COSY, NOESY, HSQC, and HMBC. The 1 H NMR spectrum of 11 showed eight aromatic proton signals, H-2 ′ ,6 ′ , H-2 ′′′ ,6 ′′′ , H-3, H-3 ′′ , H-3 ′ ,5 ′ , H-3 ′′′ ,5 ′′′ , H-8 ′′ , and H-6. Moreover, the proton signals of H-8 and H-6 ′′ were missing. Thus, 11 was identified as apigenin-(6→8)-apigenin (agathisflavone, Figure

Acknowledgments

The authors would like to thank the Department of the Southern Province which authorized us to collect plant material and the Curator of Herbarium "NOU" who provide us with an extraction of Parasitaxus usta specimens.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iD

Tsukasa Iwashina https://orcid.org/0000-0002-3497-989X

Supplemental Material

Supplemental material for this article is available online.


Pierre Laperdrix
email: pierre.laperdrix@univ-lille.fr

Naif Mehanna
email: naif.mehanna@univ-lille.fr

Antonin Durey
email: antonin.durey@univ-lille.fr

Walter Rudametkin
email: walter.rudametkin@univ-lille.fr

The Price to Play: a Privacy Analysis of Free and Paid Games in the Android Ecosystem

Keywords: mobile games, tracking, online privacy

With an ever growing number of smartphone users, the mobile gaming industry is booming and reached more than 2.6 billion players worldwide in 2020. While some mobile games charge a relatively modest fee to be played, the vast majority are free and rely exclusively on ads or tracking for their revenue streams. Over the years, Google and Apple have tightened their privacy requirements for apps. They perform thorough app scanning to detect abusive behaviours and require developers to provide a privacy policy on how they collect and handle user data. Yet, little is known about the data collection that fuels the advertising and tracking industry behind mobile games. Players can see the ads that are presented to them but they may not be aware of the invisible trackers that collect valuable data in the background.

In this study, we aim to shine a light on the tracking ecosystem in mobile games on Android and understand how different monetization models can impact user privacy. We introduce a pipeline that collects both free and paid games and we use the static analysis provided by the Exodus audit platform to detect the trackers present in them. We analyse a total of 6, 751 games, including 396 paid games. Our results show that paying for a game does not necessarily shield users from data collection. We find that 87% of free games include at least one tracker, compared to 65% of paid games that do. On average, free games have 3.4 times more trackers and request twice more dangerous permissions than paid games. We also notice that the genre of the game and its targeted audience impact the number of trackers. Games in the Casual category presents the most trackers while those in the Educational one have the least.

INTRODUCTION

Smartphones are in the pockets of 6.3 billion users, which represents more than 80% of the worldwide population [START_REF]Number of smartphone users from 2016 to 2021 -Statista[END_REF]. As every single one of these devices has the capacity to play games, the potential market for mobile gaming is huge. In 2021, 2.66 billion mobile gamers [START_REF]Number of mobile gaming users worldwide in 2021, by region -Statista[END_REF] spent collectively more than $116 billion USD on mobile games [START_REF]State of Mobile 2022 -App Annie[END_REF], surpassing the revenue of all other gaming sectors combined [START_REF]The Games Market and Beyond in 2021: The Year in Numbers -Newzoo[END_REF]. As users were faced with a global pandemic, they spent more time at home and on mobile games [START_REF]75% of Pandemic-Driven Increase in Mobile Gaming Activity Will Persist Indefinitely, According to New IDC and LoopMe Report -IDC[END_REF] with a lasting effect and further gains that can already be observed in 2021 [START_REF]Sensor Tower: Mobile game spending hit $22.2B in 2021 Q1, up 25% from 2020 -VentureBeat[END_REF].

Amidst this booming market, publishers are exploring different ways to monetize their games, as detailed by Tang [START_REF] Ailie | Mobile app monetization: app business models in the digital era[END_REF]. More than 95% of games on the Google Play Store are free [START_REF]Google Play vs the iOS App Store | Store Stats for Mobile Apps -42Matters[END_REF], while the others can be accessed for a relatively small price with most paid games being priced under $10 USD. Games can also earn revenue by showing personalized ads to players or by selling in-app purchases (IAP) (e.g., to unlock levels, buy in-game currency). Inspired by streaming services, some games also offer subscriptions in the form of battle passes [START_REF]Battle Pass: Examples in Top-Grossing Games & Best Practices[END_REF] that offer in-game rewards (e.g., extra content, boosts, skins, new levels). Although each of these methods contribute to generating revenue, as detailed by Unity's Game Report [START_REF]Gaming Report -Unity[END_REF], ads and IAPs capture the lion's share of the revenue with IAP slowly starting to surpass advertising revenues in different markets.

Interestingly, at a time where online privacy is at the forefront of discussions regarding the Web, with the development of antitracking technologies [START_REF]Intelligent Tracking Protection -WebKit[END_REF][START_REF]Latest Firefox rolls out Enhanced Tracking Protection[END_REF][START_REF]Privacy features -Brave browser[END_REF], the upcoming deprecation of third-party cookies [START_REF]An updated timeline for Privacy Sandbox milestones -Google[END_REF] and the design of privacy friendly tracking alternatives [START_REF]The future of ads and privacy -Mozilla[END_REF], mobile gaming seems to be have been saved from these discussions. On the one hand, users care about data privacy with 79% willing to spend time and money to protect their data [START_REF]Building Consumer Confidence Through Transparency and Control -Cisco[END_REF]. On the other hand, the pervasiveness of ads and trackers in mobile games seems to be the complete opposite, where users accept opaque data collection and data sharing operations performed by a lot of unknown tracking companies. A survey of US gamers conducted in 2018 revealed that 82% of users preferred free mobile games with ads compared to paid mobile ones without [START_REF]Relaxing with a Game? Enjoy an Ad -eMarketer[END_REF]. 74% would also watch an in-game ad if they get an in-app perk in return.

As ads are an integral part of mobile gaming, what implications does it have on users' privacy? What is the true privacy cost of free games? Does paying for a game up front truly ensures better privacy guarantees?

In order to answer all these questions, we analyse in this paper 6, 751 games, including 396 paid games, and compare the trackers present in them. Section 2 describes the related work. Section 3 introduces the pipeline that we built to collect our dataset of games. Section 4 presents the result of our analysis by considering different dimensions like the number of trackers, the included permissions, the category of a game, its base price and its intended audience. Section 5 discusses our findings while Section 6 concludes our paper.

RELATED WORK

In the field of Android security, there is an extensive literature on how to analyse apps, through the use of static analyses to dynamic approaches or even instrumenting firmware or proxying traffic. Some examples include using taint tracking like Flowdroid [START_REF] Arzt | Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps[END_REF], Taintdroid [START_REF] Enck | Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones[END_REF] or AndroidLeaks [START_REF] Gibler | Androidleaks: Automatically detecting potential privacy leaks in android applications on a large scale[END_REF] to capture data leaks, explore how permissions can be circumvented to collect sensitive data [START_REF] Reardon | 50 Ways to Leak Your Data: An Exploration of Apps' Circumvention of the Android Permissions System[END_REF], or simply look at detecting malware [START_REF] Lindorfer | Andrubis-1,000,000 apps later: A view on current Android malware behaviors[END_REF]. However, only a handful of studies look at the presence of trackers in mobile applications and if there are differences between free and paid applications.

Tracking in mobile applications. Razaghpanah et al. studied the mobile advertising and tracking ecosystem by analysing real-world mobile network traffic [START_REF] Razaghpanah | Apps, trackers, privacy, and regulators: A global study of the mobile tracking ecosystem[END_REF]. Thanks to an app called Lumen, installed directly on users' devices, they were able to capture where the data from mobile apps was being sent. They also traced back the parent companies behind many different tracking services and found, in particular, that Alphabet was present in over 73% of the 14,599 apps in their dataset. Finally, they discovered that 39% of the tracking services they identified were also present as third-parties in at least one of the Alexa Top 1,000 websites.

Reyes et al. analysed 5,855 of the most popular free children's apps on Android to see if they were compliant with the Children's Online Privacy Protection Act (COPPA) [START_REF] Reyes | Won't somebody think of the children?" examining COPPA compliance at scale[END_REF]. They instrumented the APIs that access sensitive resources and used Lumen to detect if data from those APIs was sent over the wire. Their results showed that 19% of tested apps collected identifiers or personally identifiable information that should never have been transmitted.

Kollnig et al. compared the same 12k apps on both Android and iOS to see if there were any differences in terms of privacy [START_REF] Kollnig | Are iPhones Really Better for Privacy? Comparative Study of iOS and Android Apps[END_REF]. In the end, they found no significant differences between the two platforms despite different architectures and requirements from both app stores. 88% of Android apps had at least one tracking library, while 79% of iOS apps did. In both stores, about 3% of apps had more than ten trackers. Android apps asked for more permissions compared to their iOS counterparts, but this was mainly due to platform differences where some resources on iOS were not gated behind a permission, contrary to Android.

Studying paid applications. There are few studies that include paid applications in their datasets, arguably because of the budget required to purchase them. In 2015, Seneviratne et al. collected the top 100 free and paid apps on Android in 4 countries [START_REF] Seneviratne | A measurement study of tracking in paid mobile applications[END_REF]. They found that 60% of the paid apps included at least one thirdparty tracking library compared to 85% of the free ones. Moreover, the tracking behaviours of free apps were about the same as for paid ones since they found the same types of trackers in both. In 2019, Han et al. compared 1,505 free Android apps with their paid versions to see if differences could be observed in terms of privacy [START_REF] Han | Do you get what you pay for? comparing the privacy behaviors of free vs. paid apps[END_REF]. About half had an identical set of permissions and third-party libraries between the free and paid version.

More recently, Watanabe et al. performed a large-scale analysis of 2M free apps and 30K paid ones to detect software vulnerabilities [START_REF] Takuya | Study on the Vulnerabilities of Free and Paid Mobile Apps Associated with Software Library[END_REF]. The price of paid apps they studied ranged from $1 USD to $200 USD. By using vulnerability scanners and checking for dead code, they found that 70% of the vulnerabilities in free apps stem from software libraries, compared to 50% for paid ones.

Finally, Ishii et al. looked at the apps present in 13 different Android marketplaces [START_REF] Ishii | Understanding the security management of global third-party android marketplaces[END_REF]. They observed that some paid apps can be found for free in other marketplaces, with some even having their license verification library removed so that the pirated copy could bypass the control for an existing license.

Our work. In this paper, we investigate the tracking ecosystem in mobile games as we believe their unique economic models can have an impact on the tracking and advertising libraries that are embedded in them. As the studies discussed above perform measurements on all types of apps without differentiation, we focus here on doing measurements specifically for mobile games. Notably, we want to see if paying for games up front is more privacy friendly than playing free games.

DATASET

In this section, we detail the dataset that we used to perform our privacy analysis, how we collected it and why we did it that way.

Collecting Android applications

Challenges. Collecting mobile games directly from the Play Store is not an easy task. Google provides no list of all the games available on the Play Store. Querying the store through the search bar returns no more than 200 applications. The lists of top applications in different categories are also limited to 200 results. And to make an exhaustive search more difficult, rate limiting is applied on requests coming from the same account and the same IP address. Viennot et al. highlight how complicated it can be to actually collect apps on a large scale with their PlayDrone crawler [START_REF] Viennot | A measurement study of google play[END_REF]. They paid participants on Amazon's Mechanical Turk to create legitimate Google accounts to circumvent rate limiting. They also rented Amazon servers to have different IP addresses and queried the store using a 1 million word dictionary to extensively explore the application space.

Collecting free games. Because of the limitations imposed by the Play Store and how costly it can be to setup a crawling infrastructure, we relied on the AndroZoo dataset provided by the University of Luxembourg to collect free games [START_REF] Allix | AndroZoo: Collecting Millions of Android Apps for the Research Community[END_REF]. This dataset is regularly For this study, we selected all of the free games from 2021 collected in the AndroZoo dataset, which includes games with as few as a dozen active users, up to popular games with millions.

Collecting paid games. In order to investigate paid games on the Play Store, we relied on Google's Play Pass [START_REF]Introducing Google Play Pass -Google[END_REF], a subscription service for games akin to Subscription Video On Demand (SVOD) services like Netflix. By paying a fixed monthly fee, users get access to hundreds of apps and games as part of their subscription and they are all "completely free of ads and in-app purchases" [START_REF]Grow with Google Play Pass -Google[END_REF]. Through the subscription, paid games can be accessed for free and free games become devoid of in-game ads and in-game purchases. For this study, we collected the 716 games included in Play Pass in November 2021. It should be added that the games downloaded as part of Play Pass are identical to those downloaded by non-Play Pass users. APKs are not built specifically for Play Pass users, everyone downloads the same APK that contains the same code and the same third-party libraries. The only difference is that the Google billing system recognizes if a user has a subscription and provides direct access to games and specific in-app products for no additional charge. This detail is especially important since we want to identify trackers that most users would be subject to and Play Pass gives us access to the proper APKs for our analysis.

Presentation of the hybrid pipeline

Figure 1 provides an overview of the pipeline we put in place to collect both free and paid games and analyse their content to identify trackers in them.

Step 1: Collection of free games. As detailed in the previous section, we relied on the AndroZoo dataset [START_REF] Allix | AndroZoo: Collecting Millions of Android Apps for the Research Community[END_REF] to collect free games. We downloaded the full list of 17M+ APKs present [START_REF]Lists of APKs -AndroZoo[END_REF] and identified the 111, 035 applications added from the Google Play Store in 2021. This includes apps released in 2021 but also updates to apps released before 2021. Because the metadata in the Andro-Zoo dataset only includes the app ID, we used a scraper called Google-play-scraper [START_REF]Google-Play-Scraper -PyPi[END_REF] to collect additional metadata for each APK directly from the Play Store, such as the app's name, rating and categories. We discarded all apps that were not games and removed the ones that were no longer available from the store. Our final list includes the APKs and metadata of 6, 035 free games available from the Play Store in 2021.

Step 2: Collection of games from Google's Play Pass. The biggest difficulty to collect all the games that are part of Play Pass is to get the actual list of what is included in the service. As Google does not provide access to the Play Pass catalogue from a Web browser, we used a Pixel 3 phone and manually added each Play Pass game to the account's wishlist. This way, we could use a Web browser to extract the IDs of all the games that are part of the service directly from the wishlist. Then, for each ID on this list, we used adb, the Android Debug Bridge, to direct the Pixel 3 phone to open the corresponding page on the Google Play Store and simulated a tap to proceed to the installation. After the game is downloaded, we extracted it from the phone for further analysis and uninstalled it. At the same time, we used the Google-play-scraper to collect the metadata available on the Play Store for each game, just as we did for the free games we collected. In total, we collected from this step 716 APKs, with 396 of them belonging to paid games.

Step 3: Analysis of APKs with Exodus. We sent all the APKs we collected in the first two steps to a local instance of Exodus, a privacy auditing platform for Android applications [START_REF]Exodus -The privacy audit platform for Android applications[END_REF]. It statically analyses the content of an APK and returns the list of embedded trackers it has found by identifying specific third-party libraries or URLs associated with tracking companies [START_REF]Exodus Tracker Investigation Platform[END_REF]. It also provides the list of the permissions required by the application. In this study, we adopt the same definition of tracker that Exodus uses: "a tracker is a piece of software meant to collect data about you or what you do". As this definition is broad, it means that the trackers reported by Exodus present different levels of privacy intrusions. An ad company that collects the user's geolocation to serve personalized ads is more intrusive than a tracker that only collects bug fixing information when a game crashes. All in all, to paint a better picture of the privacy ecosystem in mobile games, we rely on the 6 different tracker categories that Exodus provides: • Advertisements for trackers whose aim is to serve ads;

• Analytics for trackers who collect usage data;

• Crash reporters for trackers that report application crashes;

• Identification for trackers responsible for determining your digital identity. One example is logging into an app with a Facebook account through Facebook Login; • Location for trackers who determine your geographical location; • Profiling for trackers that are focused on collecting as much information as possible on the user.

Overview of our dataset. Table 1 provides an overview of the games we collected from our two sources: the free games from the AndroZoo dataset and both free and paid games from Google's Play Pass subscription service. For each game, we have the following data:

• the APK with all of the game's files;

• the Exodus report with both the list of embedded trackers and the list of permissions requested by the game; • the Play Store listing information with the game's name, the age rating, the review scores, the presence of ads, the presence of in-app purchases and the number of installations.

ANALYSIS

In this section, we aim to understand how various characteristics of a game, such as its economic model, revenue streams, genre, price or target audience influence the presence of trackers. We look at the presence of ads, the initial price of the game, its age rating and its number of users to provide insights into the tracking ecosystem in mobile games.

Impact of the economic model on tracking

4.1.1 Trackers. We find that about 2/3 of paid games have trackers, a 21% reduction compared to free games, and paid games, on average, have fewer trackers than free games. Table 2 provides an overview of the presence of trackers in all the games present in our dataset. The majority of free games have between 1 ∼ 12 trackers, while the majority of paid games have between 0 ∼ 4. Looking more precisely at the distribution of trackers in Figure 2, 10% of free games have more than 15 embedded trackers, with the highest having 36. For paid games, the top 10% have more than 5 trackers with the highest being 16. These results show as a general trend that paying for games is in general better, from a privacy point of view, but there are examples of paid games with plenty of trackers.

Permissions.

As detailed by the official Android documentation [START_REF]Permissions on Android -Google[END_REF], permissions are divided into groups with the two main ones being normal and dangerous (also called runtime permissions).

A normal permission enables access to data and actions that present little risk to the user's privacy, while a dangerous one, like the user's location, or contact list, requires explicit consent. We analysed what permissions are used by both paid and free games, with a particular focus on dangerous ones:

• Free games: 9.16 permissions on average with 1.52 being dangerous ones. • Paid games: 6.03 permissions on average with 0.73 being dangerous ones.

Table 3 shows the top permissions accessed by most free and paid games. The top 10 normal permissions shows some differences between the two. For example, the receive_boot_completed permission shows a 27% difference. According to an official Google forum [START_REF]RECEIVE_BOOT_COMPLETED permission automatically added -Google Mobile Ads SDK Developers[END_REF], a change in a dependency in the Google Mobile Ads SDK caused this permission to appear automatically in a lot of applications. Since this SDK is used for Google AdMob, the most popular tracker in our dataset (see Table 5), this results in a high use of this permission. We expect this number to even go up as developers update their SDK to the newer version that includes this dependency change. Other differences exist but the main takeaway is that free apps, on average, request the top 10 normal permissions more often.

For dangerous permissions, access to external storage is at the top for both free and paid games. As mobile games can require extra storage space for textures and assets, it is common for developers to add support for external storage to free up the internal storage. To provide some indication of how large some Android games can be, a popular game called Genshin Impact requires more than 14GB of storage. Location access is high for free games with about 15% of them accessing it, while it is less than 1% for paid games. read_phone_state is also high, with 19% of free games asking for this permission. This enables the game to access information like the user's phone number or the current cellular network. Finally, the write_settings permission can also prove to be dangerous as the game can modify system settings. In general, we see that paid games ask for less permissions than free games, resulting in less access to sensitive information.

4.1.3

In-game ads and in-app purchases. Table 4 splits our dataset into categories based on the presence or absence of in-game ads and in-app purchases (IAPs). First, the presence of either ads or IAPs shows an increase in the overall number of trackers, with an additional increase when both are present. The increase is smaller and more restrained for paid games as can be seen with the smaller averages and standard deviations. Second, there's a strong difference when free games are devoid of ads and IAPs, only 35% of them contain at least one tracker, compared to between 56% and 96% with at least one tracker for other categories. This may indicate that developers looking to monetize their apps are more likely to introduce a third-party tracker. Third, we see that the majority of paid games do not have ads or IAPs, which likely is in accordance with the expectations of consumers who pay up front for a game. However, the majority still do contain trackers. Finally, a curious observation is the presence of advertising trackers in the games that claim they do not contain any ads on the Play Store. This is possibly a limit of the static analysis which we discuss in Section 5. Some games might include advertising trackers without using them.

4.1.4 Price of games and IAP. Our dataset includes paid games with prices ranging from $0.99 USD to $35.99 USD. The vast majority of games are priced at less than $10 USD. We expected to see a significantly higher number of trackers for cheaper games, for which the price could be justified by higher expected advertising revenues, while more expensive games would need less advertising revenue due to their higher expected sales revenue. However, Figure 6 in Appendix A shows that the average number of trackers is not correlated to game prices. Indeed, both free and paid games include monetized content to increase revenue. While we previously noted that IAPs, on average, lead to the presence of a higher number of trackers in games, Figure 7 in Appendix A shows that the maximum IAP price does not seem to impact the number of trackers in the game. Similarly, with IAP prices ranging from $1.39 USD to $400 USD, the average number of trackers does not seem to be impacted by the pricing.

Tracker categories

Figure 3 provides an overview of the distribution of trackers across free and paid apps (see Section 3.2 for a short description of each category of trackers). The first observation is that advertising trackers are 5 times less present in paid games than in free games. Analytics is the most prominent category of trackers in paid apps compared to advertisements for free games. Then, regarding both profiling and identification trackers, there is a little use of them in free games and almost non-existent use in paid ones. Finally, Table 5 reports on the top 10 most popular trackers across all games. Google has 4 different entries, with each tracker having its own well-defined purpose: Google AdMob serves ads directly in games, Google Firebase Analytics is analytics mainly targeted for developers, Google Analytics 4 is aimed at marketers and includes a "Games report" analysis to provide data on user acquisition, retention, engagement and monetization [START_REF]GA4] Games reports -Google[END_REF], and Google Tag Manager for managing the tracking tags in a game. Facebook follows closely with two of their own trackers: Facebook Ads and Facebook Login. All in all, the trackers in mobile games are predominately for advertisement and analytics.

Game categories

The Play Store groups games into various categories that the developers choose based on the game's content. Our dataset holds games from 17 categories, with the least represented being the Casino category, with a total of 88 games, and the most represented being the Casual and Puzzle categories, each containing respectively 1168 and 1099 games. Figure 4 shows the average number of trackers based on the game categories. Casual, Sports and Casino games are at the top while Arcade, Music and Educational games are at the bottom.

One aspect highlighted by this graph is that the game genre has an impact on the economic model behind a game. The Casual category, which is the most represented in our dataset, also includes the highest number of trackers, with an average of over 8 trackers per game. This high number can likely be linked to what Casual games offer in terms of gameplay experience. They tend to be played more opportunistically for shorter sessions and they are uninstalled more frequently. They suffer from a lack of player fidelity and higher churn [START_REF]Mobile App Trends Report -LiftOff[END_REF], thus introducing maybe the need or the possibility for developers to compensate by serving more ads, more aggressively, to increase revenue quickly. In contrast, game genres where players are more invested long-term, typically include a lower number of trackers. This is the case of the Action and Strategy categories, which normally capture users for longer sessions. Finally, the Educational games are shown to include the least amount of trackers, likely due to the nature of their content and the targeted audience. As detailed in Section 4.5, Google imposes strict policies on what can be included in a game targeted for a younger audience. Moreover, to maintain a playful aspect and provide a solid learning environment, game makers may opt to provide less ads to limit disturbances compared to other categories aimed at a more mature audience.

Top 10 games with the most revenue

To dive deeper into how different economic models may impact tracking, we look at the top 10 games with the most revenue on the Play Store in October 2021. Table 6 provides the details on these games. Surprisingly, only 3 games out of 10 contain ads, which means they rely mostly on IAPs or subscriptions for revenue. IAPs have a very wide price range, many are under $1 USD with one being as high as $374.99 USD. Regarding trackers, the numbers observed are similar to the average numbers seen in Section 4.1.1 with all of them including analytics and 8 including an identification tracker. Regarding permissions, the numbers vary between the ten games but they all access APIs needed for various features, like access to external storage to download additional assets or to the microphone for multiplayer games that provide audio exchanges

during the game. For Pokemon GO, it has the highest number of dangerous permissions but this is also inherent to how the game works.

Pokemon GO relies on the player's location to spawn creatures and it uses the device's camera heavily for its augmented reality inter-Both permissions are gated behind explicit prompts because they are considered sensitive for users.

Finally, we can see that the game's category affects the game's source of revenue. For example, there are 3 RPGs in the top 10 that, despite having less than 10M users (compared to Candy Crush Saga's 1B+ users), have no ads and rely entirely on IAPs. This indicates that the type of mobile game can attract different player bases with different spending habits.

In the end, the top 10 provides a glimpse at how popularity and genre can impact the economic model of a game. While less popular games may rely on ads and trackers to bring in revenue, more popular games can also sustain themselves exclusively on IAPs, even in cases when they are otherwise free games.

Google's "Teacher Approved" games

With children increasingly relying on technology for both education and entertainment, Google has recently unveiled a new gaming section labelled Teacher Approved. Apps that are designed for children must participate in a larger Designed for Families program [START_REF]Designing Apps for Children and Families -Android Developers[END_REF], which opens their eligibility to be rated for the Teacher Approved program, without being guaranteed of inclusion. The program includes stricter policies that apps must follow in order to obtain the certification. These policies are verified by a panel of U.S based specialists, which includes teachers, and requires that the app's content and functionality be accessible and appropriate for children. Another major point of the Designed For Families program is that it imposes limits on advertising and tracking for children. Developers must follow the Family Policy when targetting children with ads, they can either take on these additional responsibilities when using in-house advertising, or they may use one of the self-certified ad SDKs [START_REF]Participate in the Families Ads Program -Android Developers[END_REF]. Naturally, given these restrictions, approved apps are expected to contain less tracking and advertising libraries.

Our dataset includes 181 games with the Teacher Approved label, of which 110 are available for free. Our analysis shows that 75% of those games include at least one tracker, with almost 47% including at least one advertising tracker. As these results may look surprising, they are not unexpected as Google does not restrict approved apps from including trackers, but rather requires them to abide to stricter tracking and advertising practices, including using certified ads [START_REF]Families Ads Program -Android Developers[END_REF].

Teacher Approved apps still include a significantly lower number of trackers, with, on average, 2.12 trackers per game compared to 6.11 trackers for free games. We also note that the top three trackers used in Teacher Approved games make use of Google's APIs, namely, Google Firebase Analytics, followed by Google AdMob and Google Analytics. We notice however that other advertiser's APIs are also served. Although Google requires that only self-certified ad SDKs are authorized to serve ads to children in certified apps [START_REF]Participate in the Families Ads Program -Android Developers[END_REF], we do note that 29 Teacher Approved games include trackers flagged by Exodus as Advertisement trackers that are not present in the list of Google's self-certified ad SDKs.

Figure 5 provides an overview of the average number of trackers split by different categories. It can be seen that even though profiling goes against the guidelines, trackers within this category can still be found in Teacher Approved games. We can however notice that the vast majority of trackers belong to the Analytics category, which follows with our previous observations. Overall, Teacher Approved games do seem to provide, from a privacy point-of-view, a better experience, with less ad and tracker frameworks.

DISCUSSION

Summary of findings and privacy implications

In this study, we saw that a variety of economic models are being used to monetize mobile games. Some games favour ads and inapp purchases for revenue, while others rely on the more classical approach of being purchased for an up front fee. Our analysis reveals that paying for a mobile game leads to, on average, a smaller number of trackers and little to no ads. Analytics are the most prominent form of tracking in paid games, while advertisement is very prominent in free ones. Different genres also have an effect on the number of trackers as the ways they engage users and encourage spending can vary greatly, as can be seen by the revenue models between a match-3 puzzle game and a fantasy RPG. Yet, one of the most important results from our study is how developed the tracking industry is in mobile gaming. Out of 6,751 games, more than 85% had at at least one tracker embedded in it. In terms of privacy, this number paints a bleak picture as a lot of data is being collected on what the players do and how they play. Even though not all collected data to the player's exact identity, a lot of it is still linked to a virtual identity and likely passed around for analyses and monetization between many companies. This shows that users are under constant scrutiny when using their smartphones for gaming, as a single tracker has the capacity to record anything from the smallest gestures up to getting the user's list of contacts.

Discussing monetization in mobile games would not be complete without mentioning the current state of the ad industry. As privacy is being put at the forefront of discussions about users' digital well-being, alternatives are being designed to protect users from invasive tracking techniques and it is leading to strong changes in the mobile ad ecosystem. A first example is the AppTracking-Transparency framework by Apple [START_REF]User Privacy and Data Use -Apple[END_REF]. When launching a new app, the user can chose not to share their device's advertising identifier, which means trackers cannot link the activity of the user across different apps on the same device. While this does not prevent the collection of information, it limits the creation of very large user profiles based on the data from dozens of apps. Another example leading to changes is the return of contextual advertising [START_REF]Privacy regulations and comeback of contextual advertising -Gala[END_REF]. Instead of personalizing an ad using all the information collected on the user, the ad will be based on the content of the page that the user is seeing. The intrusiveness of this technique on user's privacy is minimal as companies do not need to build and maintain user profiles based on behaviour, purchasing habits or other factors. In the end, it remains to be seen the impact that these changes will have on trackers in mobile apps and if it will indeed lessen the heavy scrutiny that users are subject to, often without their knowledge.

Limitations and future work

A first avenue for future research is to use dynamic analysis to go deeper into the analysis of tracking in mobile games. While static analysis reveals the presence of specific trackers, we do not capture how they are actually used in games and if they are triggered at all. Using a tool, such as Lumen [START_REF] Razaghpanah | Apps, trackers, privacy, and regulators: A global study of the mobile tracking ecosystem[END_REF][START_REF] Reyes | Won't somebody think of the children?" examining COPPA compliance at scale[END_REF], combined with complex scenarios to exercise the game and explore as many options as possible, would likely help us identify the information being sent, how sensitive it is, and what the final destination is.

A second avenue is to look at games provided by marketplaces other than Google's Play Store, and platforms other than Android. As each marketplace has its own requirements when submitting an app, tracker analysis would reveal if players from different marketplaces are subject to less or more tracking than those who rely on the more popular Play Store.

Finally, a third avenue is to integrate the future evolution of the Android platform in a tracking analysis. Google has just announced that in February 2022, they will show a Data Safety Section on apps in the Play Store to indicate what user data each app collects and shares [START_REF]Launching Data safety in Play Console: Elevating Privacy and Security for your users -Android Developers Blog[END_REF]. Integrating this data into our study could help refine its findings. Another evolution is the new Android App Bundle (AAB) format for Android applications [START_REF]About Android App Bundles -Android Developers[END_REF]. Designed to be more flexible than the traditional APKs, apps delivered with the AAB format will be optimized for the user's device based on its configuration and language. What remains to be seen is how tracking companies will utilize this mechanism, for example, to deliver different trackers based on the device used by the user.

CONCLUSION

Games on mobile generate revenue in different ways: by showing in-game ads, by offering in-app purchases or by having an up-front cost on the store. In this paper, we investigate how these different economic models can impact user tracking by analysing the trackers present in 6,355 free and 396 paid mobile games. Overall, we found that free games have on average 3.4 times more trackers than the studied paid games and they request twice as many dangerous permissions. While the main trackers in free games are for advertisement purposes, analytics are the most prominent trackers in paid games. We also look at games aimed at a younger audience with the "Educational" game category and the presence of a "Teacher Approved" badge. We conclude that the stricter policies imposed by Google have had a positive effect on children tracking as there are less trackers in these games than in the other studied categories.

Figure 1 :

 1 Figure 1: A representation of our pipeline to collect the applications and metadata required for our analysis.

Figure 2 :

 2 Figure 2: Distribution of trackers across free and paid games.

Figure 3 :

 3 Figure 3: Average number of trackers per categories for paid and free games.

A c t ioFigure 4 :

 4 Figure 4: Average number of trackers per game genre.

Figure 5 :

 5 Figure 5: Average number of trackers per categories for Teacher Approved and regular games.

Table 1 :

 1 Source of the games present in our dataset.

		Free games Paid games Total
	AndroZoo	6, 035	0	6, 035
	Play Pass	320	396	716
	Total	6, 355	396	6, 751

Table 2 :

 2 Overview of the presence of trackers in the games of our dataset

		Percentage of		Average number		Standard
	games with trackers	of trackers per game	deviation
	Free	86.79%			6.11		6.65
	Paid	65.31%			1.80		2.38
	1.0	Free Paid
	0.8
	0.6
	P(x)
	0.4
	0.2
	0.0
	0	5	10	15 Number of trackers 20	25	30	35

Table 3 :

 3 Top 10 normal and dangerous permissions for free and paid games. Additional information on each permission can be found in the official Android documentation[23].

	Normal

Table 4 :

 4 Overview of the presence of ads and in-app purchases (IAP) in games

	Price	Contains Ads	Offers IAP	Number of games	Number with trackers	Avg number of trackers	Stand. Dev. of trackers	Number with advertising trackers	Avg number of advertising trackers
	Free	No Yes	No Yes No Yes	694 512 3627 1469	243 (35.0%) 421 (82.2%) 3394 (93.5%) 1412 (96.1%)	2.00 5.20 6.15 8.26	3.90 4.11 6.79 7.07	156 (22.5%) 237 (46.3%) 3339 (92.1%) 1375 (93.6%)	0.74 0.92 3.12 3.79
	Paid	No Yes	No Yes No Yes	311 58 8 19	206 (66.2%) 33 (56.9%) 5 (62.5%) 15 (78.9%)	1.68 1.78 2.12 3.63	2.13 2.55 2.47 4.39	77 (24.7%) 18 (31.0%) 3 (37.5%) 9 (47.4%)	0.38 0.50 0.37 1.58

Table 5 :

 5 Top 10 most popular tracker across all games

	Tracker name	Categories	Games with this tracker
	Google AdMob	Advertisement	4622
	Google Firebase Analytics	Analytics	4110
	Unity3d Ads	Advertisement	2203
	Google Analytics 4	Analytics	1968
	Facebook Ads	Advertisement	1758
		Analytics, Profiling,
	AppLovin	Identification,	1256
		Advertisement
	Google Tag Manager	Analytics	1158
	IAB Open Measurement	Identification, Advertisement	1115
	AdColony	Advertisement	1114
	Facebook Login	Identification	1106

Table 6 :

 6 Information on the top 10 games with the most revenue on the Google Play Store in October 2021

	Revenue rank	Game category Contains ads	Range of IAP	Number of installs	Number of trackers	Number of permissions (dangerous)
	Gareena Free Fire	Action	No	$0.99 USD -$109.99 USD	1,000,000,000+	7	28 (6)
	Candy Crush Saga	Casual	Yes	$0.99 USD -$149.99 USD	1,000,000,000+	7	11 (0)
	Coin Master	Casual	No	$0.99 USD -$374.99 USD	100,000,000+	8	13 (1)
	Odin: Valhalla rising	RPG	No	$4.99 USD -$89.99 USD	1,000,000+	3	16 (1)
	PUBG Mobile	Action	Yes	$0.99 USD -$199.99 USD	500,000,000+	12	23 (4)
	Pokémon GO	Adventure	No	$0.99 USD -$99.99 USD	100,000,000+	9	26 (7)
	Roblox	Adventure	No	$0.49 USD -$199.99 USD	500,000,000+	3	13 (3)
	Genshin Impact	RPG	No	$0.99 USD -$99.99 USD	10,000,000+	6	14 (3)
	Gardenscapes	Casual	Yes	$0.49 USD -$99.99 USD	100,000,000+	11	13 (2)
	Fate/Grand Order	RPG	No	$0.99 USD -$79.99 USD	1,000,000+

• The list of game IDs used for our study.• The script to collect metadata from the Play Store.• The scripts to collect APKs from AndroZoo and the Play Pass.

ACKNOWLEDGMENTS

We thank the Exodus Privacy team for their advice in this study. This research has been partially funded by the Hauts-de-France region in the context of the ASCOT project of the STaRS framework and by the ANR FP-Locker project under grant number ANR-19-CE39-00201.

AVAILABILITY

The artifact accompanying this study can be found at https://github. com/antonin-durey/the-price-to-play and contains:

A PRICE OF GAMES AND IAP 0 . 9 9

1 . 4 9 1 . 9 9 2 . 4 9 2 . 9 9 3 . 4 9 3 . 9 9 4 . 4 9 4 . 9 9 5 . 4 9 5 . 9 9 6 . 4 9 6 . 9 9 7 . 4 9 7 . 9 9 8 . 4 9 8 . 9 9 9 .


Alexis Front

Christine Mottet
email: mottet@cinam.univ-mrs.fr

Trends of chemical ordering in Pt-based nanoalloys

Keywords: Nanoparticles of alloys, Many-body potentials, Monte-Carlo simulations

Trends of chemical ordering in Pt-based nanoalloys

Introduction

Nanoparticles of alloys called nanoalloys met a large success in the past twenty years but they have been studied a longer time ago, before the birth of what we call now "nanoscience". Indeed, already in the seventies, the metallic clusters was mentioned by Jacques Friedel [START_REF] Friedel | The Physique of Clean Metal Surfaces[END_REF] as a new state of matter between molecules and crystal, and a very active field, with many potential applications from catalysis to photographic reactions. Jacques Friedel has probably inspired Fernand Spiegelman in two ways: not only because of his interest for clusters but also as a great defender of tight binding models, in solid state theory, to describe the energetics of many different systems. Fernand Spiegelman used such theory to describe single metals or noble metals, and recently, whereas density functional theory would like to keep being in the foreground, he developped in collaboration with Mathias Rapacioli, Nathalie Tarrat and coworkers the density-functional tight-binding method [START_REF] Spiegelman | Density-functional tight-binding: basic concepts and applications to molecules and clusters[END_REF] which is at the frontier of the two approaches, also at the frontier between theoretical chemistry and physics. Whereas Jacques Friedel organized the first "International Symposium of Small Particles and Inorganic Clusters" (ISSPIC) in Lyon in 1976, before a long series of ISSPICs up to now, the second one has been chaired by Jean-Pierre Borel, in Lausanne, in 1980. Jean-Pierre Borel was the co-author with Philippe Buffat of the spectacular "size effect on the melting temperature of gold particles" [START_REF] Ph | Size effect on the melting temperature of gold particles[END_REF], which was one of the first manifestation of the modification of physical properties with size. It inspired a lot of studies, both in theory and in experiments, where nanoparticles made of some tens to some thousands of atoms have shown facinating properties as giant magnetic moments or superparamagnetism, surface plasmon resonnance, higher catalytic properties than bulk phase. In the late nineties, I invited Fernand Speigelman to join the jury of my PhD devoted to the study of mono and bimetallic nanoclusters by numerical simulations [START_REF] Mottet | PhD thesis on the Study by numerical simulation of free mono and bimetallic clusters[END_REF]. By associating two different metals in one nanoparticle it was possible to join all the advantages of the alloys with the specificity of the finite size effect. It has given rise to a large field of research from theory to application [START_REF] Johnston | Nanoalloys: From Theory to Application[END_REF][START_REF] Ferrando | Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles[END_REF][START_REF]Nanoalloys: Synthesis, Structure and Properties[END_REF]. In the present review we will gather the results on three Pt-based systems with different chemical trends to show how the simple characteristics of the system can give reliable predictions on the chemical arrangement on nanoparticles and some guides for the phase diagrams of nanoalloys. The paper will be composed of four sections, including this introduction and the conclusion, the second section being devoted to the description of the energetic model and numerical simulations, and the third one to the results on the Pt-based nanoalloys i.e. Pt-Co, Pt-Ag and Pt-Pd in the shape of truncated octahedra.

Energetic model and numerical simulations

The theoretical approach is based on a semi-empirical potential derived from the electronic structure in the tight binding approximation: the so-called sec-ond moment approximation (SMA) of the density of states which is fitted to ab initio density functional theory (DFT) calculations both for the pure metals and for the mixed interactions.

The SMA interatomic potential writes as follows [START_REF] Rosato | Thermodynamical and structural properties of fcc transition metals using a simple tight-binding model[END_REF] for an alloy with atom types a and b:

E a i = - j,rij
where the first term represents the attractive part with a square root dependence with the neighboring atoms and the second term, the repulsive part with a pairwise Born Mayer form. In this expression, r ij is the distance between the atom at site i and their neighbors at site j, r c ab is the cut-off distance and r 0 ab is the first neighbor distance depending on the nature of the atoms. Then, p ab , q ab , A ab , ξ ab are four parameters which can be fitted to the experimental or to the DFT values of lattice parameters, cohesive energies, surface energies, elastic constants for the pure metals and to the mixing energy or to the formation energies of ordered phases for the mixed interactions.

Then we use Monte Carlo simulations in the semi-grand canonical (S-GCMC) ensemble to optimize the chemical configuration on the clusters. Starting from truncated octahedral structure, we propose random atomic permutations and small random atomic displacements around each atom position. The difference between the two chemical potentials remains constant, together with the pressure, the temperature and the total number of atoms, and the concentration evolves toward the equilibrium concentration.

The Metropolis sampling ensures attainment of a Boltzmann distribution of the chemical configurations at equilibrium, this means when the number of Monte Carlo trials is sufficient to get reliable averages of physical quantities. In the Metropolis Monte Carlo, a trial is accepted if it lowers the total energy of the system. If not, it can still be accepted with the probability equal to e (-∆E kT ) where ∆E is the energy difference between the configurations before and after the trial, k being the Boltzmann constant and T the temperature.

3 Pt-X nanoalloys with X=Co, Ag, Pd These three systems present typical chemical tendencies: a strong ordering tendency for Co-Pt, no chemical tendency which means a solid solution in all the concentration range for Pt-Pd, and a phase separation tendency in the Ptdiluted Pt-Ag system but with a peculiarity to display one ordered compound at the equiconcentration. The mixing enthalpies of these systems are ploted in Fig. 1. The highest, in absolute value, the mixing enthalpy, the highest the tendency to form ordered compounds.

These general tendencies have a great impact on the chemical ordering on nanoalloys as we will show in the following. Let's start with the highest ordering tendency illustrated by the Co-Pt system. We performed Monte Carlo simulations in the whole concentration range of 1289 and 2951 atoms Co-Pt clusters. Some of the obtained configurations are illustrated in Fig. 2 where we can see typical ordered phases which are the one present in the bulk phase diagram. To characterize the ordered phases we plotted the occupation of the four fcc sublattices in the bulk and in the two cluster sizes. We observe in the bulk that the four sublattices are well separated in domains where at low Pt concentration the four sublattices are equally occupated, this means the phase is a desordered Co-rich solid solution. Then, one over four is fully occupied by Pt, the other three by Co, this corresponds to the L1 2 (Fig. 2-a) at 25% of Pt corresponding to the Co 3 Pt compounds in bulk. This phase is made of an alternance of pure and mixed layers along the (100) direction. The domain of existence of this phase is extended up to around 40% of Pt. Then, two over four of the sublattices are occupied by Pt, the other two by Co, this corresponds to the L1 0 phase (Fig. 2-b) at equiconcentration corresponding to the CoPt compounds in bulk. This phase is made of an alternance of pure layers along the (100) direction and is stable in a domain of concentration between 40 and 60% of Pt. Then the same L1 2 phase but Pt-rich corresponding to the CoPt 3 compounds in bulk is stable in a domain between 60 and 80% of Pt(Fig. 2-c). Finally the four sublattices are equally occupied, this is the Pt-rich solid solution. The tricky thing when we pass from the bulk to the clusters is that in the cluster shape, the system have to accommodate the finite size with a given morphology, notably the coexistence of (100) and (111) facets, and possible surface superstructures which does not exist in bulk.

In fact, the core of the nanoparticles follows very well the different bulk ordered phases i. cated because as in the infinite Co-Pt surfaces [START_REF] Front | Bidimensional phases in Co-Pt surface alloys: A theoretical study of ordering and surface segregation[END_REF], the Co-Pt (111) facets present the ( √ 3 × √ 3)R30 • (Fig. 2-(g)) with a slight Pt enrichment at the surface as compared to the p(2x2) (Fig. 2-(a)) which is the proper structure in a (111) L1 2 layer with 1/4 or 3/4 of Pt. In the ( √ 3 × √ 3)R30 • , the concentration at the surface is of 33 or 66% whereas the core is around 25 or 75%. This slight surface segregation at the (111) facets of the cluster with the L1 2 core leads to a chemical desordering of this phase below the surface which can be seen by the dispersion in the sublattice occupation in the L1 2 domain for the 1289 atoms cluster (see Fig. 2). When the size increases, from 1289 to 2951 atoms, the core is sufficiently large to be relatively ordered and the subsurface contribution is less important. To conclude, this behaviour is exactly a chemical ordering frustration in the cluster because of finite size effect. Now let's go on with the Pt-Ag system for which there is both an ordering tendency and a phase separation tendency in bulk alloys. Indeed the bulk alloy at equiconcentration forms the ordered L1 1 phase, made of an alternance of pure atomic layers along the (111) direction. But except these ordered phase, the phase diagram [START_REF] Sluiter | Ab initio calculation of the phase stability in Au-Pd and Ag-Pt alloys[END_REF] presents large miscibility gaps between this ordered phase and the two diluted solid solutions. We notice also that the dissolution of silver in platinum is particularly limited (about 1-2% at 800 K) whereas it is larger on the other side (up to 20% of Pt in Ag). These are some peculiarity of the Pt-Ag system that we have well reproduced in our semi-empirical atomistic potential [START_REF] Pirart | Reversed size-dependent stabilization of ordered nanophases[END_REF][START_REF] Front | A theoretical study of Pt-based nanoalloys: Co-Pt, typical ordering system, and Pt-Ag, hybrid system between ordering and demixion[END_REF][START_REF] Front | Stress effect in L1 1 nanophase stabilization in Pt-Ag nanoalloys[END_REF]. Now if we look at the chemical configuration of a truncated octahedron of 1289 atoms (TOh 1289 ) (see Fig. 3) we can get two different structures in competition: one with the perfect L1 1 ordered phase in the core and an atomistic surface shell pure in silver (designated by L1 1 @Ag) or an other one with the same L1 1 phase in the core but limited by concentric shells of alternately Ag and Pt (designated by L1 1 @Pt@Ag). We have shown in ref. [START_REF] Pirart | Reversed size-dependent stabilization of ordered nanophases[END_REF] that the most perfect L1 1 phase is only stable for relatively small size (below one thousand of atoms). This leads to conclude to a reversed sizedependent stabilization of ordered nanophase. Indeed, we observed that by increasing the cluster size, the extension of the L1 1 nanophase in the core is accompanied by a strong stress on the silver atomic layers in the core, as illustrated in Fig. 3 by the red-orange colors. The structure can release this stress by the alternation of concentric pure atomic shells from the surface as shown by the structure L1 1 @Pt@Ag. From the energetic point of view, this structure remains stable for larger sizes.

In that case we conclude that the chemical ordering in nanoalloys can be frustrated at larger size which is not the usual observation since in most of the finite systems, by increasing the size, we recover the bulk alloy properties. In the Pt-Ag system we have both an ordering and phase separation tendency, which means a tendency to form an ordered phase but also in the same time a tendency to surface segregation, which is a classical way to phase separate. By increasing cluster size, the ordered phase is limited by the coexistence of the two different driving forces: the ordering tendency leading to the L1 1 ordered phase in the core and the phase separation tendency leading to the Ag surface -0.6 0.6 0.0 15 GPa -6 GPa L11@Ag L11@Pt@Ag Stress maps (GPa) Fig. 3 Cutting views of the Pt-Ag TOh 1289 (Pt atoms in blue and Ag atoms in grey) with different chemical arrangement: the L1 1 @Ag structure with a perfect L1 1 core and a pure silver surface shell and the L1 1 @Pt@Ag one with a L1 1 central core surrounded by Ag@Pt subsurface shells and a final silver surface shell. The color maps represent the equivalent stress map with a color scale from higher compressive stress in red to lower tensile stress in blue. On the right, the energy difference between the two different structure, a positive sign means the L1 1 @Pt@Ag is the most stable one. Finally the local atomic stress distribution for the two structures. All theses results are taken from ref. [START_REF] Pirart | Reversed size-dependent stabilization of ordered nanophases[END_REF].

segregation, these two effects resulting in an increasing stress with increasing size, which is defavorable to the well ordered L1 1 phase. Finally let us consider the Pt-Pd system where the chemical tendency is rather low, which leads to a solid solution all across the composition range in the bulk alloy. In the nanoalloys, we observe a Pd surface site segregation starting from the vertices, then the edges, then the (100) and finally the (111) facets. The hierarchy of the segregation follows typically the coordination number: the segregation starts at the less coordinated sites up to the highest coordinated ones. These segregation isotherms are plotted in the first column graphs in Fig. 4 for the two cluster sizes of 405 and 1289 atoms. In the second column, we plotted the Pd concentration on the different concencentric shells starting from the surface up to the center. Even if there is almost no chemical ordering tendency in the Pt-Pd bulk alloy, in the clusters core we observe an alternance of pure Pd, pure Pt shells starting from the enrichment of the surface shell in Pd. This was already obtained by F. Calvo with similar model [START_REF] Calvo | Solid-solution precursor to melting in onion-ring Pd-Pt nanoclusters: a case of second-order-like phase change ?[END_REF] on icosahedral structure that he called "onion-ring" structure. It has to be noticed that by increasing cluster size, the concentric alternative layers concern only the surface neighborhood and are not propagated in the core as shown by DeClercq et al. [START_REF] De Clercq | Pd surface and Pt subsurface segregation in Pt-Pd nanoalloys[END_REF] and as we can see on Fig. 4 for the 1289 atoms size for which the central shells are mixed. Only the surface shell, the subsurface and the shell 1 are alternatively filled with Pd or Pt atoms. This onion-ring structure at the vicinity of the surface is a result of a slight tendency to Pd surface segregation because it has a lower surface energy.

Conclusions

Looking at three different Pt-based nanoalloys we described very different chemical arrangements that can be linked to their bulk alloy characteristics.

In the case of a strong ordering tendency as the Pt-Co system, we find that the truncated octahedron cluster displays the ordered phases as in the bulk with some surface reconstructions on the (111) facets which are also the ones predicted on the (111) infinite surface [START_REF] Front | Bidimensional phases in Co-Pt surface alloys: A theoretical study of ordering and surface segregation[END_REF]. It is the (

√ 3 × √ 3)R30
• which represents a slight Pt surface segregation of 0.33 or 0.66 depending on whether we are in the Co-rich or Pt-rich side, to be compared with the p(2x2) surface ordering at 0.25 and 0.75 with the same stoechiometry at the surface and in the bulk. In finite size systems, this surface reconstruction leads to a slight chemical desordering in the layers below the surface which is more visible as the cluster size is small. Despite experimental studies on these systems, which clearly show the core ordering at equiconcentration [START_REF] Alloyeau | Size and shape effects on the order-disorder phase transition in CoPt nanoparticles[END_REF][START_REF] Andreazza | Probing nanoscale structural and order/disorder phase transitions of supported Co-Pt clusters under annealing[END_REF][START_REF] Andreazza | Structure and order in cobalt/platinum-type nanoalloys: from thin films to supported clusters[END_REF], there is no precise determination of the surface chemical ordering which is out-of-reach by actual experimental tools.

For a system with both chemical ordering and phase separation tendency, depending on the concentration, things are different. This is the case of the Pt-Ag system for which there is a strong Ag surface segregation that enters in competition with the core ordering according to the L1 1 phase at equiconcentration as in the bulk. Such behaviour has clearly been observed by High resolution Transmission Electron Microscopy [START_REF] Pirart | Reversed size-dependent stabilization of ordered nanophases[END_REF]. By increasing the size, the L1 1 core with the Ag pure surface shell becomes too much stressed because the rombohedral deformation associated to the L1 1 phase is constrained by the silver shell. The cluster can release the tress by filling a subsurface shell with Pt with a lower atomic radius, which is not accessible by experiments up to now. However we demonstrated a reversed size-dependent stabilization of ordered nanophases [START_REF] Pirart | Reversed size-dependent stabilization of ordered nanophases[END_REF].

Finally, for a system with no particular phase ordering or phase separation tendency, which means a solid solution in all the composition range as the Pt-Pd system, we show that nanoclusters display a local onion-ring structure, localized near their surface, and a solid solution in their core.

To conclude, the nanoalloys chemical arrangements can be rich and various and it is of prime importance to control the distribution of the different species in the system in order to optimize the physical and chemical properties of these nanoobjects which find more and more applications for industrial or medicinal purposes.

Fig. 1

 1 Fig. 1 Mixing enthalpies for the three different systems: Co-Pt, Ag-Pt and Pd-Pt. Experimental values from Hultgren et al. in Selected Values of the Thermodynamic Properties of Binary Alloys, American Society for Metals, Berkeley (1981) for Pd-Pt and Co-Pt systems, estimated values from Many-body potential fitted to ab initio calculations for Ag-Pt.

Fig. 2

 2 Fig. 2 Snapshots of the Co-Pt TOh nanoparticles with 25% (a), 50% (b), 66% (c) and 75% (d) of Pt (Co atoms in brown and Pt atoms in blue). The graphs represent the fcc sublattices occupations of the core of the TOh 1289 , the TOh 2951 and the bulk alloy as a function of Pt concentration from ref. [9]. The pictures on the right represent the (111) surface reconstructions that we observe on the (111) facets of the clusters with the p(2x2) in (e), the p(2x1) in (f) and the ( √ 3 × √ 3)R30 • in (g).

Fig. 4

 4 Fig.4Snapshots of a 405-atom truncated octahedra of Pt N Pd M at 100 K (Pt in blue and Pd in grey) and the corresponding segregation isotherms as a function of the chemical potentiel for the TOh 405 (first row) and for the TOh 1289 (second row) for the surface sites and the concentric core shells, taken from ref.[START_REF] De Clercq | Pd surface and Pt subsurface segregation in Pt-Pd nanoalloys[END_REF].

Acknowledgements The authors warmly thank Bernard Legrand, Guy Tréglia and Riccardo Ferrando for fruitful discussions and acknowledge support from the International Research Network-IRN "Nanoalloys" of CNRS. This work was granted access to the HPC ressources of IDRIS under the allocation 2017-096829 mage by GENCI.

Conflict of interest

The authors declare that they have no conflict of interest.