Pierre Brissot
email: pierre.brissot@univ-rennes1.fr

Optimising the diagnosis and the treatment of iron overload diseases

A number of human disorders are related to chronic iron overload, either of genetic or acquired origin.

The multi-organ damage produced by iron excess leads, in adults and in children, to severe clinical consequences, affecting both quality of life and life expectancy. The diagnosis is increasingly based on a non-invasive strategy, resorting to clinical, biological and imaging data. The treatment rests on either venesection or chelation therapy, depending on the etiology. Major advances in the fields of molecular biology, pharmacology, and biotechnology pave the road for key improvements in the diagnostic and therapeutic management of the patients.

Iron overload diseases encompass a large spectrum of genetic and acquired disorders. The symptoms induced by chronic iron excess are close, whatever the etiology, and may cause significant morbidity and mortality representing a worldwide health problem. The diagnosis of iron overload is now based on a non invasive approach, resting on clinical, biological and imaging data. The treatment, which depends on the mechanisms underlying the development of iron overload, is mainly based on venesection or chelation therapy. However, in light of major advances in the pathophysiological understanding of these diseases [START_REF] Brissot | Iron metabolism and related genetic diseases: A cleared land, keeping mysteries[END_REF], a number of promising innovative therapeutic approaches are emerging.

1. SPECTRUM OF IRON OVERLOAD DISEASES (Fig. 1 C282Y homozygosity should be considered as a mere predisposition for the disease since phenotypic expression is quite variable (due to incomplete penetrance of the gene). It has thus been reported that approximately only 1% of homozygote women and less than 30% of homozygote men may develop a fully expressed form of the disease [START_REF] Allen | Iron-overload-related disease in HFE hereditary hemochromatosis[END_REF], compromising both quality of life and life expectancy. Type 1 HC is an adult disease, most often expressed after 30 and 40 years old in men and women, respectively. It should also be noted that some data have pointed out the possible physical advantage provided by HFE mutations [START_REF] Cippa | Increased height in HFE hemochromatosis[END_REF][START_REF] Hermine | Eighty percent of French sport winners in Olympic, World and Europeans competitions have mutations in the hemochromatosis HFE gene[END_REF].

1.1.1.2. Non HFE-related HC. They are rare diseases, but present in both caucasian and non-caucasian populations [START_REF] Bardou-Jacquet | Non-HFE hemochromatosis: pathophysiological and diagnostic aspects[END_REF][START_REF] Wallace | The global prevalence of HFE and non-HFE hemochromatosis estimated from analysis of next-generation sequencing data[END_REF]. i) Type2 HC corresponds to a severe form of iron overload occurring in younger individuals (less than 30 years old), and therefore called juvenile HC. It is due to mutations of the hemojuvelin (HFE2 or HJV) gene or of the HAMP (hepcidin) gene (corresponding to types 2A and 2B HC, respectively). ; ii) Type3 HC is due to mutations of the transferrin receptor 2 (TFR2) gene. Usually expressed in adults, it may also cause juvenile HC ; iii) Type4 HC. Due to mutations of the ferroportin (SLC40A1) gene, it is the only form of HC with a dominant mode of transmission. In its usual form (called type A), the mutations alter the export function of ferroportin. In the type B form, the mutations alter the hepcidin receptor function of ferroportin, leading to a refractoriness state toward hepcidin ; iv) Hereditary aceruloplasminemia (HA) (see below) may also be classified among non-HFE related HC.

1.1.1.3. Compared prevalences of HFE and non-HFE HC. Types 1, 2 and 3 HC are very rare as compared to HFE-HC. It has been reported that the predictive frequency of HFE pathogenic genotypes was approximately 1/1000, versus 1/5000 000 for type 2A HC, 1/6000 000 for type 3 HC, and 1/180 000 000 for type 2B HC. Unexpectedly, the frequency for type 4 HC was close to that of type1 HC (1/1300), partly due to the dominant nature of the disease(9).

ACQUIRED IRON OVERLOAD DISORDERS

1.1.2.1. Iron supplementation. Chronic iron deficiency is a frequent worldwide condition [START_REF] Lopez | Iron deficiency anaemia[END_REF]. It may be due to iron dietary deficiency, especially in the frame of malnutrition, and requires usually oral iron supplementation. This type of oral supplementation is not prone to induce iron overload, given the physiological limitations of duodenal iron absorption. In contrast, in parenteral iron administration, such as required in patients who undergo hemodialysis for chronic renal failure, iron excess can develop if iron therapy overcomes the needs [START_REF] Ribeiro | Iron therapy in chronic kidney disease: Recent changes, benefits and risks[END_REF].

1.1.2.2. Transfusions. Repeated transfusions are a major cause of iron overload [START_REF] Porter | The pathophysiology of transfusional iron overload[END_REF].

Three main types of hematological diseases necessitate multiple transfusions :

transfusion-dependent thalassemia, myelodysplastic syndromes, and aplastic anemia related to the therapeutic regimen applied in bone marrow stem cell transplantation. -Hepcidin deficiency is the prevailing mechanism in types 1, 2 and 3 HC as well as in dyserythropoiesis. The common mechanistic denominator is the decreased hepatic production of hepcidin which is the master regulator of systemic iron [START_REF] Pigeon | A new mouse liver-specific gene, encoding a protein homologous to human antimicrobial peptide hepcidin, is overexpressed during iron overload[END_REF][START_REF] Nicolas | Lack of hepcidin gene expression and severe tissue iron overload in upstream stimulatory factor 2 (USF2) knockout mice[END_REF][START_REF] Ganz | Systemic iron homeostasis[END_REF]. This decrease is due, in HC, to the involved mutations and, in dyserythropoiesis, to the action of the bone marrow hormone erythroferrone (ERFE) [START_REF] Kautz | Identification of erythroferrone as an erythroid regulator of iron metabolism[END_REF][START_REF] Kautz | Erythroferrone contributes to recovery from anemia of inflammation[END_REF]. The subsequent decreased plasma hepcidin concentration induces both an increased duodenal iron absorption, and an increased release by the spleen of the iron coming from the normal degradation of senescent erythrocytes (erythrophagocytosis). The consequence is a chronic elevation of plasma iron concentration. Part of this iron is, as physiologically expected, taken up by its plasma carrier protein, transferrin, forming transferrin-iron.

Transferrin-iron is essentially delivered to the bone marrow in order to contribute to the production of new red blood cells. But, due to the high levels of plasma iron, the quantity of circulating transferrin is insufficient to bind all of the metal, so that iron becomes partially present under another form, called non-transferrin bound iron (NTBI) [START_REF] Hershko | Non-specific serum iron in thalassaemia: an abnormal serum iron fraction of potential toxicity[END_REF][START_REF] Brissot | Non-transferrin bound iron: a key role in iron overload and iron toxicity[END_REF]. NTBI, which is likely bound to low molecular weight ligands such as citrate and acetate but also to albumin, has the very special kinetic property to be very avidly taken up, not by the bone marrow, but by various parenchymal cells, especially in the liver (hepatocytes), pancreas, and heart. Therefore, NTBI is considered as the main responsible for iron excess development in many organs.

-Hepcidin refractoriness (type4B-HC). Although plasma hepcidin concentration is not decreased, the down-stream cellular consequences are similar to those occurring in quantitative hepcidin deficiency.

-Iatrogenic excessive body iron intake : It corresponds to excessive parenteral administration of iron, either as iron per se (intravenous infusions) or through multiple transfusions, the erythrocytes being very iron rich (they represent half of the total body iron content).The fate of parenteral iron is to be stored within the reticuloendothelial system (macrophages), namely essentially in the spleen. This storage process is very fast for infused iron and postponed to the end of erythrocyte life for transfusional iron. Iron is subsequently released into the blood stream, leading to increased transferrin saturation and to NTBI production. Iron is known for its involvement in the Fenton reaction leading to ROS production [START_REF] Jomova | Advances in metal-induced oxidative stress and human disease[END_REF]. The iron species involved in this toxic reaction is not transferrin-iron, but a special NTBI component, named labile plasma iron (LPI) [START_REF] Esposito | Labile plasma iron in iron overload: redox activity and susceptibility to chelation[END_REF][START_REF] Lan | Redox active plasma iron in C282Y/C282Y hemochromatosis[END_REF][START_REF] Cabantchik | LPI-labile plasma iron in iron overload[END_REF][START_REF] Cabantchik | Labile iron in cells and body fluids: physiology, pathology, and pharmacology[END_REF]. This iron species is able to target cellular membranes and nuclei, and is considered the main culprit for iron-related cellular and organ damage. -Iron toxicity depends on the cellular type affected by iron deposition.

Thus, iron is more damaging to parenchymal cells (typically the hepatocytes), than to reticuloendothelial cells (typically the macrophages).

-The liver is a critical cross road in the process of body iron toxicity :i) as a major iron storage organ, it is capable, for a long period, to protect many other organs from becoming iron overloaded ; ii) however, when this role of reservoir is overwhelmed by the duration and intensity of iron overload, the liver may loose its « bulwark » property, opening the road for extra-hepatic iron toxicity ; iii) in chronic liver failure -whatever its etiology-the decreased production by the hepatocytes of transferrin and hepcidin(28) may favor body iron overload and toxicity. Indeed, both mechanisms contribute to increase plasma transferrin saturation (through decreased plasma transferrin concentration and increased iron concentration, respectively), leading to the formation of plasma NTBI and LPI ; iv) the liver is a preferential target of iron toxicity. Being a storage organ is in fact a double-edged sword with the risk of progressive iron-related hepatocyte damage, leading to fibrosis, cirrhosis, and even hepatocellular carcinoma.

-The role of cofactors acting as modulators of iron overload and toxicity is increasingly acknowledged. These factors can be environmental (acquired), such as menstruations or pregnancies which can contribute to decrease iron overload, or chronic alcoholism which may aggravate hepatic iron toxicity [START_REF] Fletcher | Hemochromatosis and alcoholic liver disease[END_REF]. The cofactors can also be genetic. In HFE-HC, the presence of a mutation in the hepcidin promoter is associated with massive iron overload [START_REF] Island | A new mutation in the hepcidin promoter impairs its BMP response and contributes to a severe phenotype in HFE related hemochromatosis[END_REF]. Moreover, in this disease, GNAP(31), TMPRSS6 [START_REF] Valenti | Effect of the A736V TMPRSS6 polymorphism on the penetrance and clinical expression of hereditary hemochromatosis[END_REF], and PCSK7 [START_REF] Stickel | Evaluation of genome-wide loci of iron metabolism in hereditary hemochromatosis identifies PCSK7 as a host risk factor of liver cirrhosis[END_REF] polymorphisms have been reported to aggravate iron overload, to decrease iron overload, and to accentuate hepatic fibrosis , respectively.

2. DIAGNOSTIC ASPECTS (Fig. 2)

GLOBAL DIAGNOSTIC STRATEGY

It is a multi-step process.

CLINICAL APPROACH

It is the prerequisite of any diagnostic search, and should explore systematically general health and the various organs, since chronic iron overload corresponds to a systemic disease. Thus, the main syndromes, more ou less associated, are : chronic fatigue, joint pains, bone demineralization, melanodermia (increased skin pigmentation), liver signs (hepatomegaly, moderate transaminase increase), diabetes, or cardiac symptoms (rhythm disturbances, cardiac failure).

BIOCHEMICAL APPROACH

2.1.2.1. Plasma ferritin concentration. Usually defined by plasma ferritin values over 300 µg/L in men and 200 µg/L in women, hyperferritinemia is the major firstline biochemical parameter to explore total body iron stores. Whereas normal or low ferritinemia rules out iron overload, hyperferritinemia is highly suggestive of, but not necessarily synonymous with, iron overload. Indeed, it is essential to interpret rigorously hyperferritinemia since it can be related to a number of causes without significant body iron excess [START_REF] Deugnier | Hyperferritinemia not related to hemochromatosis[END_REF]. Among those situations : i) inflammation since ferritin is an acute-phase reactant protein.

The macrophagic activation syndrome [START_REF] Larroche | Hemophagocytic lymphohistiocytosis in adults: diagnosis and treatment[END_REF] is very close to this mechanism.

Occurring in some infectious, hematological or auto-immune diseases it can be expressed by dramatic levels of hyperferritinemia ; ii) cytolysis, especially during acute or chronic hepatitis ; iii) alcoholism ; iv) metabolic hyperferritinemia which is likely to represent the most frequent cause of hyperferritinemia [START_REF] Deugnier | Dysmetabolic iron overload syndrome: a systemic disease?[END_REF]. Usually associated to plasma levels below 1000 µg/L, metabolic hyperferritinemia is diagnosed on a number of convergent arguments : a) normal transferrin saturation (≤45%) ; b) diversely associated metabolic symptoms, such as overweight, increased arterial blood pressure, non-insulin dependent diabetes, hypercholesterolemia, hyperuricemia, and hepatic steatosis (as shown by ultrasound examination) ; c) absent or mild liver iron overload. V) The correlation of high plasma ferritin levels with organ iron burden depends on the cellular distribution of excessive iron deposits. Indeed, it has been reported, especially when comparing transfusion-dependent and non-transfusion dependent thalassemias, that similar levels of hyperferritinemia correspond to lower hepatic iron concentrations in transfusion-dependent thalassemia. [START_REF] Musallam | Iron-chelating therapy for transfusional iron overload[END_REF] This may be due to the higher propensity of macrophages to produce ferritin. ii) quantifying iron overload (histological semi-quantification and/or biochemical determination fo liver iron concentration) ; iii) orientating the etiology (hepatocyte versus macrophage iron deposition) ; iv) assessing liver fibrosis, and iv) searching for cofactors of hepatic toxicity, such as features of metabolic syndrome or alcoholism.

2.2.OPTIMISING THE DIAGNOSTIC STRATEGY

DIAGNOSING IRON OVERLOAD HAS BECOME A NON INVASIVE APPROACH

Whereas, for a long time, performing a liver biopsy was an indispensable step, it is no more so for the following reasons :

-Proving, quantifying, and assessing indirectly cellular distribution (via the balance between liver and spleen iron deposition) can be accurately achieved by MRI.

-Transient elastography is increasingly performed, as a non invasive surrogate for liver biopsy, to assess liver fibrosis(47).

NEW PROMISING BIOLOGICAL TOOLS ARE EMERGING

-A number of recent biochemical parameters should, in the near future, be more and more applied to investigate disorders of iron metabolism. Among them, the measurements of plasma NTBI, LPI, and possibly hepcidin concentrations.

-The recent advances in molecular biology, especially through next generation sequencing (NGS), are already transforming the genetic diagnosis for ironrelated disorders [START_REF] Mcdonald | Next-generation sequencing: Application of a novel platform to analyze atypical iron disorders[END_REF]. This high throughput approach is a powerful way to identify mutations. However, it raises interpretation issues since it is able to identify a number of new variants of which it is difficult to know whether or not they are harmful.

CLINICAL UTILITY TO PROPOSE THE CONCEPT OF HEPCIDIN-DEPRIVATION SYNDROME (HDS)

The HDS syndrome, observed in all cases of HC (but type A ferroportin disease) and in dyserythropoiesis, can be characterized by the association of increased plasma transferrin saturation, parenchymal (hepatocyte) iron deposition, and absence of splenic iron overload. It exposes to severe complications of iron overload, due to the parenchymal LPI impact.

SETTING UP NATIONAL AND INTERNATIONAL REFERENCE CENTERS AND

NETWORKS, for improving the diagnosis of iron overload disorders related to rare HFE or non-HFE mutations, should be part of the health decision-maker policies.

3.THERAPEUTIC ASPECTS (Fig. 3) 3.1.2.5. Efficacy. All three compounds are efficient. DFP has an interesting efficacy on cardiac iron overload [START_REF] Baksi | Randomized controlled trials of iron chelators for the treatment of cardiac siderosis in thalassaemia major[END_REF]. DFX is able to negativate iron balance and to exert beneficial hepatic effects on iron-related hepatic inflammation and fibrosis(56).

3.1.CURRENTLY AVAILABLE THERAPEUTIC TOOLS AND STRATEGIES

FAMILY SCREENING (GENETIC DISEASES)

3.1.3.1. General strategy. Family screening is based on the following three main data : i) the genetic profile that has been identified in the proband and serves as a marker to evaluate genetic predisposition among the relatives, ii) the phenotypic markers, combining plasma transferrin saturation and ferritin levels, and iii) the recessive or dominant mode of transmission of the disease.

Differential strategies :

-Quantitative hepcidin deficiency-related HC (types1, 2, or 3 HC). i) Type1

(C282Y/C282Y)-HC [START_REF] Porto | EMQN best practice guidelines for the molecular genetic diagnosis of hereditary hemochromatosis (HH)[END_REF]. Only C282Y homozygosity exposes to the disease risk, keeping however in mind that homozygosity does not mean « disease » (due the partial penetrance of the gene).

Siblings (genetically explored only after the age of 18) are the primary at risk relatives.

However, given the high HFE mutation prevalence, it is recommended to screen genetically also the offspring (after age 18). For the parents, it is usually sufficient to check the phenotypic markers, limiting the genetic study to the cases with phenotypic suggestion of established iron excess. ii) Type2 -HC. Being exceptional, recessive, and juvenile diseases, screening should focus on the siblings (whatever their age). iii) Type3-HC. The screening strategy is close to that of Type1-HC with the differences that, being a rare disease, the offspring are not at risk, and that, since young individuals may be affected, siblings should be explored whatever their age.

-Types4 A and B -HC. The screening procedure should consider both the identical dominant mode of transmission, and the distinct phenotypic expression of these two types of ferroportin diseases. -Practical measures should be reinforced to diagnose and treat the disease as early as possible. They include : i) Increased awareness of the disease among the medical community, general population and health authorities, with the critical help of patient associations ; ii)

Developing family screening centers which are necessary when considering the frequent geographical scatttering of the relatives ; iii) Setting up national and international reference centers in the field of genetic iron overload disorders ; iv) As to the mass screening of caucasian populations, it remains debated but seems a reasonable preventive goal in view of the following data : HFE-HC is a frequent disease, with a long clinically asymptomatic phase, an easy and non invasive diagnosis, and furthermore an effective treatment.

-From the medical and scientific viewpoint, preventive improvements should come from two main directions : one is to identify predictive markers of phenotypic expression, another one is to investigate the interest of hepcidin supplementation, especially for preventing iron overload development in early diagnosed (and potentially expressing) homozygotes, and to avoid iron overload reconstitution after the end of the induction phase.

ACQUIRED DISEASES (TRANSFUSIONAL IRON OVERLOAD)

Associating plasma iron, transferrin saturation and ferritin determination together with MRI assessment of iron overload, should permit to start the chelation therapy as early as possible.

In conclusion, the iron overload field concerns a wide variety of potentially severe diseases of genetic or acquired origin. Key advances in the knowledge of iron metabolism joined to major biological, pharmacological and technological improvements have considerably improved the diagnostic and therapeutic approaches of these diseases. The active ongoing research in these domains is opening highly promising roads for continuous amelioration of patient care.

EXPERT COMMENTARY

In contrast to iron deficiency which is widely recognized as a major health problem, iron overload disorders remain poorly known to the general population and even to the medical community, probably largely due to the fact that iron remains, in our collective unconscious, synonymous with strentgh and good health. It is therefore important to disseminate informations concerning the major recent advances in the diagnostic and therapeutic management of these iron overload disorders which, whatever their genetic or acquired origin, may severely impact the quality of life and life expectancy of the affected adults or children.

FIVE-YEAR VIEW

-For genetic iron overload, especially related to hepcidin deficiency, many new variants will be discovered and it is hoped that some of them will permit to predict the phenotypic expression of the genetic predisposition. This prediction should ideally concern not only the amount of iron overload expected in the absence of treatment but the target organs likely to be affected in case of iron excess. An earlier diagnosis, thanks to increase awareness of the diseases and to extended phenotypic screening (based on plasma iron and/or transferrin saturation and/or ferritin levels), should, together with novel predictive markers of phenotypic expression, permit to initiate preventive therapy, ideally consisting of hepcidin supplementation.

-For transfusional iron overload, the diagnosis should benefit from greater accessibility to MRI permitting to start the treatment as soon as possible. One cannot exclude that novel oral iron chelators will be developed during this period but defining the best combined strategies with the existing chelators appear the most promising orientation.

Whether hepcidin supplementation will have a significant role in the therapeutic approach of this type of iron overload is a very stimulating perspective.

KEY ISSUES

• Iron is crucial for life but iron overload, like iron deficiency, can be severely deleterious for the human body.

• Iron overload can be of genetic or acquired origin. Hemochromatosis and transfusional iron overload are the respective archetypes of the corresponding diseases.

• Hemochromatosis is due to either increased iron entry in the cells or decreased cellular iron egress. Hepcidin and (usual) ferroportin deficiencies are the respective mechanisms underlying these two types of hemochromatosis.

• Iron overload due to hepcidin deficiency is characterized by high plasma iron and transferrin saturation together with parenchymal (essentially hepatocyte) iron deposition.

• Plasma NTBI (non-transferrin bound iron) and its LPI (labile plasma iron) component are new iron species, responsible for iron overload and iron toxicity, respectively.

• MRI (magnetic resonance imaging) has become the non-invasive technique of reference for assessing hepatic (and splenic) iron load.

• Next generation sequencing is transforming the genetic diagnostic strategy and should be handled by expert centers.

• Venesection therapy remains the basis for removing iron excess in diseases related to hepcidin deficiency and could be, in the future, associated with (or replaced by) hepcidin supplementation.

• Chelation therapy has greatly benefited from the introduction of oral chelators.

Combining different chelators represents a promising therapeutic perspective.

  1. GENETIC IRON OVERLOAD DISORDERS They correspond to various forms of hemochromatosis (HC)(2, 3). 1.1.1.1. HFE-related hemochromatosis. Also called Type 1-HC, it is by far the most frequent form of genetic iron overload disease. The usual underlying genotype is homozygosity for the C282Y mutation(4) (new HFE terminology p.Cys282Tyr). Type 1 HC is exclusively present in the caucasian (white) population, with a prevalence of homozygosity close to 3 per thousand.

  short half-life(20-30 min). Deferiprone (DFP) (Ferriprox©) has been the first oral chelator. It is a bidentate compound (three molecules are needed to bind one iron atom) with a relatively short half-life (1.5-2.5h). Its elimination is essentially through the urinary route. Deferasirox (DFX) (Exjade©) is the most recent oral chelator. It is a tridentate compound (two molecules for binding one iron atom) with a long half-life (10-16h). It is essentially eliminated through the biliary and then fecal route.3.1.2.4. Tolerance. The major DFO drawback is it way of administration. The chelator is usually administered as a prolonged subcutaneous infusion, using a portable pump, 12 hours a day, at least five days a week, therefore raising important compliance issues, especially in children. DFP requires a three daily dose administration (a liquid formulation has been designed to avoid taking too many tablets) and may induce, exceptionally but unpredictably, agranulocytosis, therefore leading to a systematic monitoring, on a weekly basis, of the blood cell count. DFX presents the advantage of being taken only once a day. It can result in, usually transient, renal failure (creatinine increase), gastrointestinal symptoms (expected to be lessened by a new formulation), skin rashes, or transaminase increase.

FIGURE 1 .

 1 FIGURE 1. Iron overload disease spectrum. BMSCT : bone marrow stem cell transplantation.

FIGURE 3 .

 3 FIGURE 2 -

FIGURE 3 -

 3 FIGURE 3 -

  FIGURE 2. Schematic strategy for the diagnostis of iron overload diseases. M: men ; W: women. Black liver or spleen : heavy iron overload (appearing black due to very low MRI signal) ; Grey liver: moderate iron overload ; White liver or spleen: no iron overload (normal MRI signal).* Clinical symptoms mean: chronic fatigue, joint and bone symptoms, hepatic, pancreatic or cardiac symptoms. ** after having ruled out common causes of hyperferritinemia (metabolic syndrome, inflammation, cytolysis, alcoholism).

	FIGURE 1 -
		Dyserythropoiesis
		. Thalassemias
		and other iron
		loading anemias
		. Myelodysplasia
		. Hereditary
		stomatocytosis
		and other
		syndromes
	)	with chronic
	. Myelodysplasia	compensated
		hemolysis

Financial and competing interests disclosure

The author has served as an occasional consultant and speaker for Novartis laboratories and has received research founding from this pharmaceutical company. He has also received grants from EuroIron1 (European Community Grant), IronReg (French National Research Agency) and the Afemers Association. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.


Mourad Benmalek
email: mbenmalek@esi.dz

Yacine Challal
email: ychallal@esi.dz

Abdelmadjid Bouabdallah
email: madjid.bouabdallah@hds.utc.fr

Scalable multi-group key management for Advanced Metering Infrastructure

Keywords: Advanced Metering Infrastructure (AMI), Smart Grid (SG), Security, Key Management

Advanced Metering Infrastructure (AMI) is composed of systems and networks to incorporate changes for modernizing the electricity grid, reduce peak loads, and meet energy efficiency targets. AMI is a privileged target for security attacks with potentially great damage against infrastructures and privacy. For this reason, Key Management has been identified as one of the most challenging topics in AMI development. In this paper, we propose a new Scalable multi-group key management for AMI (SAMI) to secure data communications in an Advanced Metering Infrastructure. It is a key management scheme that can support unicast, multicast and broadcast communications based on an efficient multi-group key graph technique. An analysis of security and performance, and a comparison of our scheme with recently proposed schemes show that our solution induces low storage overhead (reduction reaches 83%) and low communication overhead (reduction reaches 99%) compared to existing solutions.

I. INTRODUCTION

S MART GRID (SG) refers to the next generation power grid in which the electricity distribution and management is upgraded by incorporating advanced two-way communications and pervasive computing capabilities for improved control, efficiency, reliability and safety [START_REF] Yan | A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges[END_REF]. A practical example of the benefits of introducing the smart grid includes the greater availability of electricity to homes at a lower cost, and the integration of distributed and renewable power generation such as local solar and wind generators [START_REF] Fadlullah | Toward Intelligent Machine-to-Machine Communications in Smart Grid[END_REF].

Advanced Metering Infrastructure (AMI) is an essential part of the smart grid. It inherits the two-way communication capability of smart grid and introduces new opportunities for consumers and suppliers: it is responsible for collecting all the data and information from loads and consumers, and it is also responsible for implementing control signals and commands to perform necessary control actions [START_REF] Mohassel | A survey on Advanced Metering Infrastructure[END_REF]. The critical role of AMI in the smart grid has made this system a privileged target of cyber attacks. Consequently, AMI security is of very high importance for the security of the smart grid.

In general, the fundamental security requirements of AMI are: confidentiality, integrity, and availability [START_REF] Cleveland | Cyber Security Issues for Advanced Metering Infrasttructure (AMI)[END_REF]. Privacy of the customer's sensitive data like metering and energy consumption is the most important issue of confidentiality in AMI, customers do not want unauthorized people or marketing firms to know how much energy they are using, what their pattern of energy usage is, or other energy-related information. Integrity in AMI is very important for both meter reading stored in smart meters or transmitted over the communication channels and control commands such as Demand Response (DR) mechanisms that enable customers to cut down energy usage at peak times for example. Unlike traditional systems, availability of information and control commands generated and managed by AMI is compulsory for the operation of the whole smart grid which includes too many meter readings being exchanged between smart meters and utility system.

To meet these security requirements, cryptographic countermeasures must be deployed to protect data integrity and confidentiality for AMI. However, cryptographic mechanisms for AMI require also an efficient key management. Inadequate key management can result in possible key disclosure to attackers, and even jeopardizing the entire goal of secure communications in AMI. Therefore, key management is a critical process to ensure the secure operation of AMI.

Several key management schemes (KMS) have been proposed [START_REF] Kamto | Light-weight key distribution and management for Advanced Metering Infrastructure[END_REF][START_REF] Yan | A secure and reliable in-network collaborative communication scheme for advanced metering infrastructure in smart grid[END_REF][START_REF] Li | Multicast authentication in the smart grid with one time signature[END_REF][8][START_REF] Wu | Fault-tolerant and scalable key management for smart grid[END_REF][START_REF] Xia | Secure Key Distribution for the Smart Grid[END_REF][START_REF] Liu | A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid[END_REF][START_REF] Wan | SKM: Scalable Key Management for Advanced Metering Infrastructure in Smart Grids[END_REF], but none of them can completely satisfy the security requirements mentioned previously. Hence, we propose a new key management scheme for AMI based on an efficient and scalable multi-group key graph technique to secure unicast, multicast, and broadcast communications in a smart grid network while achieving the security requirements of AMI.

The remainder of this paper is organized as follows. We discuss related works in Section II. In Section III we study the architecture and characteristics of messages of a AMI and the key management function requirements. In Section IV we present our key management scheme. We give a security and performance analysis of our KMS in Section V. Finally, we draw our conclusions in Section VI.

II. RELATED WORKS

In recent years, several schemes have been proposed to secure communications for AMI in smart grid. According to [START_REF] Shein | Security Measures for Advanced Metering Infrastructure Components[END_REF], key management has been identified as a fundamental security challenge in an AMI. Kamto et al. [START_REF] Kamto | Light-weight key distribution and management for Advanced Metering Infrastructure[END_REF] proposed a key distribution and management scheme for large customer networks to achieve authentication, privacy and data confidentiality in AMI. The proposed scheme is computationally expensive because of relying on Diffie-Hellman (DH) [START_REF] Diffie | New directions in cryptography[END_REF] key exchange and a group IDbased mechanism [START_REF] Shamir | Identity-based cryptosystems and signature schemes[END_REF]. Furthermore, this scheme only secures communications between HAN (Home Area Network) devices and the gateway.

Yan et al. [START_REF] Yan | A secure and reliable in-network collaborative communication scheme for advanced metering infrastructure in smart grid[END_REF] proposed an integrated approach in which trust services, integrity and data privacy could be provided by mutual authentications. In [START_REF] Li | Multicast authentication in the smart grid with one time signature[END_REF], Li and Cao proposed a onetime signature scheme to address the problem of preventing message forgery attacks in multicast communications. The proposed scheme presents a significant reduction in the storage and communication overhead, but only focuses on communication integrity and do not address confidentiality.

Nicanfar et al. [8] developed a key management protocol for data communication between the utility server and customers' smart meters based on the concept of ID-Based public/private key pair model [START_REF] Shamir | Identity-based cryptosystems and signature schemes[END_REF]. Although the proposed key management protocol aims to reduce the computation overheads, the synchronization process still demands considerable computation efforts. Wu and Zhou [START_REF] Wu | Fault-tolerant and scalable key management for smart grid[END_REF] combines symmetric key technique based on the Needham-Schroeder authentication protocol [START_REF] Needham | Using encryption for authentication in large networks of computers[END_REF] and elliptic curve public key technique [START_REF] Miller | Use of Elliptic Curves in Cryptography[END_REF] to provide a novel key management scheme for smart grid assuring strong security, fault-tolerance, efficiency and scalability. In the work of Xia and Wang [START_REF] Liu | A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid[END_REF], the authors showed that Wu and Zhous scheme is vulnerable to the man-in-the-middle attack and proposed an improvement for this scheme based on a trusted third party. However, these two schemes do not support secure multicast communications that play an important role and have wide applications in SG.

Recently, a key management scheme is proposed by Liu et al. [START_REF] Liu | A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid[END_REF] to secure unicast, multicast, and broadcast communications in AMI. This scheme based on the key graph management approach [START_REF] Wong | Secure group communication using key graphs[END_REF] suffers from a lack of scalability due to inefficient key management that results in nonnegligible communication overhead for such a large-scale system. Moreover, we found that Liu's et al. scheme is not tolerant to packet loss. Wan et al. [START_REF] Wan | SKM: Scalable Key Management for Advanced Metering Infrastructure in Smart Grids[END_REF] proposed an improvement for Liu's et al. scheme that combines an adapted identity-based cryptosystem [START_REF] Chen | Identity based authenticated key agreement protocols from pairings[END_REF] and one-way function tree (OFT) approach [START_REF] Mcgrew | Key establishment in large dynamic groups: Using one-way function trees[END_REF] for multicast key management. The use of an OFT separately for each DR project (DR projects are programs designed to decrease electricity consumption or shift it from on-peak to off-peak periods depending on consumers' preferences) results in non-negligible overhead for key storage.

None of the proposed KMS can completely satisfy the aforementioned requirements (confidentiality, integrity, and availability). Some schemes are not designed to secure multicast or broadcast communications that are frequently used in AMI, and some others result in non-negligible storage or communication overhead. Hence, we propose a new efficient and scalable key management scheme that meet the security requirements of AMI.

III. AMI ARCHITECTURE AND SECURITY REQUIREMENTS

In this section we analyze AMI system structure and interactive messages exchanged via AMI communication networks, to identify the basic requirements that are relevant to key management.

A. AMI Architecture

An AMI is composed of (Fig. 1): 1) Smart Meters (SMs): Which are electrical meters providing two-way communications, automated meter data collection and outage management. They also allow dynamic pricing, and joining/leaving Demand Response pricing projects for load control.

2) Distributed Energy Resources (DERs): Are small scale renewable electricity generation systems for family use and energy storage.

3) Gateways (GWs): Implement protocol conversion and communications between two heterogeneous networks, like the in-home network and wide area network.

4) Wide Area Communication Infrastructure: It supports bidirectional communication between costumers domain and the utility system. Different architectures and medias can be used like power line communication system, cellular networks, or IP-based networks [START_REF] Sauter | End-to-End Communication Architecture for Smart Grids[END_REF].

5) Meter Data Management Systems (MDMS): Acts as a database system for storing, managing, and further analyzing metering data in order to propose dynamic pricing, better customer service, DR and energy consumption management purposes.

B. AMI Interactive message characteristics

Interactive messages in AMI include metering data, release of DR projects, joining/leaving a DR project, electricity pricing, remote load control and notifications (loss or restoration of power).

Messages can be categorized into three classes according to their transmission mode: unicast, broadcast, and multicast.

• Unicast communication: is used when messages are transmitted from one point to the other one, for instance when a SM reports its power consumption statistics and estimated future energy demand to the utility system. • Broadcast communication: is used when messages are transmitted from one point to all the other points. A typical example of a broadcast notification message includes the real-time electricity pricing information sent from utility system to all SMs. • Multicast communication: is used when messages are transmitted from one point to a subset of the other points, e.g., a remote load control message from utility system to SMs which subscribed to the same DR project.

C. KMS Function Requirements

As KMS is a critical subsystem of the whole AMI security architecture, and given the above characteristics of AMI interactive messages, we summarize in what follows the basic requirements for an effective KMS for AMI security: The key management framework should support the three tansmission modes in AMI: unicast, multicast and broadcast. For each mode, methods of key generating, refreshing, and distribution policies must be designed clearly.

2) Scalability: It represents a major issue for such a largescale system consisting of millions of SMs.

3) Efficiency: We consider three aspects: computation, storage, and communication because of their impact on the overal system performance. The KMS processes should be computationally efficient as well as memory-usage efficient meeting the scarcity of computation and storage capacities in SMs. The processes of key generation, distribution, usage, and refreshment should also induce low communication overhead, which is important to time-critical scenarios in AMI.

4) Backward and forward secrecy: Users participating in DR projects are not fixed. Any user can join or leave any DR project at any time. For this reason, it is obvious that the forward and the backward sercery [START_REF] Wong | Secure group communication using key graphs[END_REF] should be guaranteed. The forward sercery implies that previously used secret keys and messages must be inaccessible by the new users who participate in a DR project, and the backward secrecy means that the future secret keys and messages must be inaccessible by users who leave a DR project.

5) Collusion freedom: Any set of users that unsubscribe a DR project should not be able to deduce the current used group key.

IV. SCALABLE MULTI-GROUP KEY MANAGEMENT FOR ADVANCED METERING INFRASTRUCTURE: SAMI

We introduce a new scalable and efficient key management scheme that we call Scalable multi-group key management for Advanced Metering Infrastructure (SAMI). It is based on a multi-group key graph structure that supports the management of multiple Demand Response projects simultaneously for each customer. We will demonstrate later that this new structure scales to large smart grids with dynamic Demand Response projects membership while meeting smart meters' constraints in terms of memory and bandwidth capacities.

A. Assumptions

1) The Advanced Metering Infrastructure complies with the architecture illustrated in Fig. 1. The MDMS denotes the management side and it is responsible for key generation and rekeying, and it is well protected from attacks.

2) A specific default DR project is mandatory for all users of the SG, i.e. all users are subscribed to this default DR project. This default DR project will be used by MDMS to broadcast control messages or information to all customers of the SG.

3) Except the mandatory DR project, any user can join or leave any DR project at any time.

B. Initialization of the KMS

Let us consider a set of n smart meters. Initially, a specific method of securely exchanging cryptographic keys over a public channel is used to establish individual keys between the MDMS and smart meters (For example, we can use the Elliptic Curve Diffie-Hellman ECDH key agreement [22] that is known to induce less overhead compared to many exiting end-to-end key establishment using standard Diffie-Hellman protocol). Theses individual keys {k 1 , ..., k n } will be refreshed periodically and will be used in two ways. The first one is to secure unicast communications between MDMS and the SMs, and in the other one they are used for generating the multigroup key graph for secure multicast communications.

Moreover, The MDMS must generate a group key GK 0 (refreshed periodically) for the default DR project. This key will be generated and transmitted through secure channels for each SM, and will be used to secure messages transmitted in broadcast mode. In Table I, we summarize the terminology that we will use throughout the remaining of this paper.

C. Group Key Management

In our solution, we propose a secure, efficient and scalable management of group keys in the AMI system. To address the scalability issue, key graph techniques are mostly used in the literature. Specifically, we adopt OFT [START_REF] Mcgrew | Key establishment in large dynamic groups: Using one-way function trees[END_REF] (One-Way Function Trees) which is an improvement of LKH protocol (Logical Key Hierarchy) proposed in [START_REF] Wong | Secure group communication using key graphs[END_REF] that allows to reduce the number of rekey messages.

In OFT, the MDMS and all users individually compute the group key GK. The keys of interior nodes are recursively computed from the keys of their children rather than attributed by the MDMS using the formula:

k i = f (k right(ki) ) ⊕ f (k lef t(ki) ) (1)
The result of applying the one-way function f to a key k: f (k), is called the blinded key version of k. A user only knows his individual key and the blinded keys of the sibling nodes of the nodes on his path to the root node, and these keys allow him to compute its ancestors. An example of OFT is illustrated in Fig. 2. However, as the users can subscribe to multiple DR projects at the same time, an intuitive solution is to use a key tree for each DR project. Hence, if a user u i subscribes to two or more DR projects simultaneously (e.g. DR j and DR k ), he needs to manage two sets of keys. As a result, this naive application of OFT may be costly and induces a non-negligible key storage overhead.

To reduce storage and communication costs in key management, we propose a novel multi-group key graph structure. The idea of our new key graph technique is to allow multiple DR projects to share a new set of keys.

1) Multi-group Key Graph Structure: Our multi-group key graph structure can be modeled as shown in Fig. 3: in the lower level, each OFT represents a set of users with the same first DR project subscription, the leaf node of the tree is a user's individual key and tree's root is the DR project's group key. The graph in the upper level represents combinations of root keys for users subscribing to multiple DR projects at the same time. Our multi-group key graph has the following properties: (a) a user only belongs to one OFT in the multi-group key graph corresponding to his Home DR project (first DR project subscription). He holds a copy of his leaf secret key and all keys corresponding to the nodes in the path from his leaf to the root in this tree; (b) a user has all group keys of the other DR projects to which he is subscribed; (c) if a user leaves his Home DR project and remains subscribed to one or more DR projects, he will shift to a new OFT (this OFT will be the tree corresponding to his new Home DR). These features ensure that a user will not subscribe and pay for the same DR project multiple times.

An example of the key graph is given according to Fig. 3, the MDMS provides 4 DR projects. Some users subscribe to only one DR project (e.g. u 2 subscribes only to DR 1 ), while other users may subscribe to multiple DR projects simultaneously (e.g. u 1 subscribes to DR 1 , DR 2 , and DR 3 ). In this figure, no user subscribes to both DR 2 and DR 3 at the same time. We next illustrate both member join and leave procedures executed by the MDMS when receiving a member join or leave request.

2) Rekeying operations: In our solution, when a user subscribes or unsubscribes to/from a DR project, rekeying consists of 3 operations: joining/leaving a OFT, shifting among trees, and receiving new keys for new subscriptions.

a) Leave procedure: The leave procedure deals with the case when a user unsubscribes from a DR project (u i leaves DR j ). Let φ j = {u l /u l subscribed to DR j }, Let X jk = {u l /u l ∈ φ j and Home DR

(u l ) = DR k }, Let ω k = {u l /u l ∈ X jk and DR k ∈ set(u i )}, Let δ k = {u l /u l / ∈ X jk and DR k ∈ set(u i )}.
• Case 1: We consider a user who subscribed to one or multiple DR projects and leaves his Home DR project: The MDMS updates and renews keys according to Algorithm 1.

Algorithm 1 : Update keys when user leaves Home DR Function leaveHomeDR (ui, DRj) ; 1: Apply standard OFT approach in DRj tree to update GKj (GK j represents the new group key); 2: If N sub(ui) = 1 : 3:

MDMS → X jk :

Enc(Enc(GK j , GK k ), GKj)

4: Else : 5: MDMS → ω k : Enc(Enc(GK j , k right(GK k ) ), GKj) Enc(Enc(GK j , k lef t(GK k ) ), GKj) 6: MDMS → δ k :
Enc(Enc(GK j , GK k ), GKj)

7:

Shift user ui to OFT tree corresponding to his second subscription DRx using standard OFT apporach (without updating key GKx that ui already has)

Example: Let us consider the key graph in Fig. 3. When u 5 (user who subscribed to DR 1 , DR 2 and DR 3 ) leaves DR 1 : (a) standard OFT approach is used to replace keys in the key tree corresponding to DR 1 : the two leaf nodes (k 5 and k 4-5 ) will be removed from the key tree and new individual key k 4 is established between the MDMS and u 4 :

MDMS → {u 6 , u 7 } : Enc(f (k 4 ), k 6-7 ) (2) MDMS → {u 1 , u 2 , u 3 } : Enc(f (k 4-7 ), k 1-3 ) (3) (b) update GK 1 for users in ω k : MDMS → ω 2 : Enc(Enc(GK 1 , k 8-10 ), GK 2 ) (4) Enc(Enc(GK 1 , k 11-13 ), GK 2 ) (5) MDMS → ω 3 : Enc(Enc(GK 1 , k 14-15 ), GK 3 ) (6) Enc(Enc(GK 1 , k 16-17 ), GK 3 ) (7) (c) update GK 1 for users in δ k : MDMS → δ 4 : Enc(Enc(GK 1 , GK 4 ), GK 1 ) (8)
(d) shift u 1 the OFT key tree corresponding to DR 2 which becomes his new home DR project using standard OFT approach (without updating GK 2 that u 1 already has).

• Case 2: We consider a user who is subscribed to multiple DR projects and leaves one DR project which is not his Home DR project (u i leaves DR j ) The MDMS updates and renews keys according to Algorithm 2.

Let

DR x = Home DR(u i ), Let ψ k = {u l /u l ∈ ω k and DR k = DR x }, Let π i = {k l /k l = right(k c ) or k l = lef t(k c ), k c ∈ P ath(u i )}.
Algorithm 2: Update keys when user leaves DR project Function leaveDR (ui, DRj) ; 1: Update GKj (GK j is the new group key); 2: MDMS → Xjj :

Enc(GK j , k right(GK j ) ) Enc(GK j , k lef t(GK j ) ) 3: MDMS → Xjx : kα∈π i Enc(Enc(GK j , kα), GKj) 4: MDMS → ψ k : Enc(Enc(GK j , k right(GK k ) ), GKj) Enc(Enc(GK j , k lef t(GK k ) ), GKj) 5: MDMS → δ k : Enc(Enc(GK j , GK k ), GKj)
Example: When u 1 (user who subscribed to DR 1 , DR 2 and DR 3 ) leaves DR 2 : (a) update GK 2 for users in X 22 :

MDMS → {u 8 , u 9 , u 10 } : Enc(GK 2 , k 8-10 ) (9)
MDMS → {u 11 , u 12 , u 13 } : Enc(GK 2 , k 11-13 )

MDMS → ψ 3 : Enc(Enc(GK 2 , k 14-15 ), GK 2 ) (12)
MDMS → ψ 3 : Enc(Enc(GK 2 , k 16-17 ), GK 2 ) (13)
(d) update GK 2 for users in δ k :

MDMS → δ 4 : Enc(Enc(GK 2 , GK 4 ), GK 2 ) (14)
b) Join procedure: The join procedure deals with the case when a user subscribes to a new DR project (u i joins DR j ). The MDMS apply the join rekeyin Algorithm 3. Send the new group key GK j to ui ; 4:

Send a notification to all users in φj about the application of the one-way function; 5: Else : 6:

Send a notification to all users in X jk about the application of the one-way function; 7:

Apply standard OFT approach in DRj tree whithout updating GKj.

V. PERFORMANCE EVALUATION In this section we present a security and performance analysis of our solution and prove its safety and efficiency.

A. Security Analysis 1) Forward and Backward Sercery: The proposed key management scheme supports both backward secrecy and forward secrecy. When a new user joins a DR project, he cannot learn previous group keys because he does not have access to previous node keys that were used to compute these group. When a user leaves a DR project, all affected keys (those known by the departing user in both lower and upper level) will be changed and redistributed securely which prevents the departing user from having access to the new keys and hence forward secrecy is preserved.

2) Collusion freedom: Any set of users unsubscribed from a set of DR projects cannot deduce the current used DR projects keys, because all affected keys when any user leaves a DR project will be updated and new keys are independent.

B. Performance Analysis 1) Storage Cost: We approximate the storage cost with the number of symmetric keys stored in the MDMS/SMs, and used for unicast, broadcast and multicast transmissions (individual keys, group keys and broadcast key). We compare our scheme with the schemes proposed in [START_REF] Liu | A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid[END_REF] and [START_REF] Wan | SKM: Scalable Key Management for Advanced Metering Infrastructure in Smart Grids[END_REF] (Table II).

2) Communication Cost: Our solution relies on an efficient multi-group key graph structure. Rekey operations (join, leave, and shift) introduce extra rekey cost. In the joining/leaving scenario, even though the number of group members and subscribed DR projects are the same, the number of keys to be updated varies according to the positions of the joining/leaving member in the multi-group key graph.

a) Leave procedure: According to Algorithm 1 and Algorithm 2, the communication cost in the worse cases will be as follows:

• Case 1: When u i leaves his Home DR project DR j (user subscribed only to one DR project) :

comCost = (h j + N pr -1)|K| (15)
|K| : the size of the key in bit.

• Case 2: When u i leaves his Home DR project DR j (user subscribed to multiple DR projects at the same time):

comCost = (h j + 2.A + B + h k )|K| + c (16)
A = N sub(u i ) B = N pr -N sub(u i ) h k : the hight of the new Home DR project.

The " + c" term is to specify on which group key we must apply the one-way function c = log 2 (N pr ).

• Case 3: When u i leaves one DR project DR j which is not his Home DR project DR l :

comCost = (2 + h l + 2.A + B)|K| (17)
b) Join procedure: According to Algorithm 3, the communication cost will be as follows:

• Case 1: When u i joins his Home DR project DR j :

comCost = h j |K| + c (18)
• Case 2: When u i joins a new DR project DR j which is not his Home DR project DR l : * n is the number of SMs, Npr is the number of DR projects, mj is the number of j th DR project members, N sub(ui) is the number of DR projects to which subscribes user ui.

comCost = |K| + c (19)
3) Simulation: A typical user session starts by a join event, which can be followed by one or more join/leave events to/from other DR projects. At the end of a membership in a DR project, a user leaves this DR project. We will consider a session of 24 months. Average arrival rate λ is of 10000 users/month, and average membership duration µ is 4 months. We will use a 128b long symmetric keys, and balanced OFT trees. Storage and communication costs of Liu's et al. and SKM+ KMS are readily obtained from [START_REF] Liu | A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid[END_REF] and [START_REF] Wan | SKM: Scalable Key Management for Advanced Metering Infrastructure in Smart Grids[END_REF].

• Simulation Results :

Storage Cost:

For MDMS, the storage cost can be afforded using special key servers. In contrast, the storage capacity of SMs is limited to 4-12 KB [START_REF] Wang | Cyber security in the Smart Grid: Survey and challenges[END_REF]. Fig. 4 shows a comparison of average storage cost induced at SMs between the three schemes with respect to the number of subscribed DR projects. We considered a 100.000 fixed size of DR projects. We can see that in our scheme, a SM stores fewer keys than that in [START_REF] Wan | SKM: Scalable Key Management for Advanced Metering Infrastructure in Smart Grids[END_REF] (reduction reaches 83% while a user can subscribe to 15 DR projects at the same time) and little more keys than that in [START_REF] Liu | A Key Management Scheme for Secure Communications of Advanced Metering Infrastructure in Smart Grid[END_REF]. This can be explained as follows: the scheme proposed by Liu et al. does not adopt a key graph technique, a SM stores one key for each subscribed DR project which induces a high communication overhead even the storage overhead remains relatively low. In SKM+, authors used a OFT tree for each DR project, the number of keys stored will increase significantly when a user subscribes to new DR projects. Whereas, in SAMI we see that the number of subscribed DR projects does not affect significantly the storage cost. Fig. 6 (a) and (b) shows a comparison of average communication cost per event (join/leave) with respect to the number of subscribed DR projects at the same time. We assume that there are 100.000 users (in average) subscribing to each DR project. We notice that the bandwidth overhead due to a join is the same as a leave for the Liu's et al. scheme and it is remarkably higher than that of scheme because of the inefficient multicast key management. In SAMI, bandwith overhead reduction reaches 99% with respect to Liu's et al. scheme while a user can subscribe to 15 DR projects simultaneously. Note that although SKM+ has less communication overhead than SAMI for join/leave event, the difference is not significant and it is too little to be seen in the Fig. 6

VI. CONCLUSION

In this paper, we proposed a new key management scheme for AMI in smart grid. It is an efficient and scalable key management scheme supporting unicast, multicast, as well as broadcast communications. The proposed scheme use a novel multi-group key graph technique that supports the management of multiple Demand Response projects simultaneously for each customer and induces low storage overhead compared to existing solutions without increasing the communication overhead. Moreover, the proposed KMS guarantees both forward and backward secrecy.

Fig. 1 :

 1 Fig. 1: System structure of AMI

  a and b Enc(M, k) Message M encrypted with key k HM AC k (c) Keyed-hash using k as the key ⊕ Mixing function such as bitwise exclusive-or (XOR) A → B : M A sends a message M to B

Fig. 2 :

 2 Fig. 2: Example of OFT key tree illustrating the ancestors and their corresponding sibling nodes of member u 2

Fig. 3 :

 3 Fig. 3: Example of our multi-group key graph structure

Algorithm 3 :

 3 Update keys when user joins a DR project Function joinDR (ui, DRj) ; 1: GK j = H(GKj); 2: If N sub(ui) ≥ 1 : 3:

Fig. 4 :

 4 Fig. 4: Average storage cost in SMs according to number of subscribed DR projects

  Fig.6(a) and (b) shows a comparison of average communication cost per event (join/leave) with respect to the number of subscribed DR projects at the same time. We assume that there are 100.000 users (in average) subscribing to each DR project. We notice that the bandwidth overhead due to a join is the same as a leave for the Liu's et al. scheme and it is remarkably higher than that of scheme because of the inefficient multicast key management. In SAMI, bandwith overhead reduction reaches 99% with respect to Liu's et al. scheme while a user can subscribe to 15 DR projects simultaneously. Note that although SKM+ has less communication overhead than SAMI for join/leave event, the difference is not significant and it is too little to be seen in the Fig.6(a).

15 Fig. 5 :Fig. 6 :Fig. 7 :

 15567 Fig. 5: Average storage cost with respect to DR projects' size

TABLE I :

 I Notation TableHome DR(u i ) First DR project to which subscribes user u i set(u i ) Set of DR projects to which subscribes user u i

	Notation	Description
	H(.)	A One-way hash function
	n	Number of SMs
	m Npr	Number of DR projects
	N sub(u i )	Number of DR projects to which subscribes user u i
	DR i	The ith DR project
	GK i	Group key of DR i
	P ath(u i )	All keys corresponding to the nodes in the path from u i 's individual key to Home DR(u i ) Group key
	right(k i )	Right children of node k i in the tree
	lef t(k i )	Left children of node k i in the tree

i Number of the i th DR project members

h i Height of i th OFT h i = log 2 (m i )

TABLE II :

 II Storage Cost

	Scheme			MDMS	Storage Overhead	SM i
	Liu's et al., 2013 [11]		n + Npr + 1	N sub(u i ) + 2
	SKM+, 2014 [12]	2	Npr j=1	(m j -1) + 1	N sub(u i ) j=1 (log 2 m j +1)+1
	SAMI	2	Npr j=1	(m j -1) + 1	log 2 (|Home DR(u i )|)+ N sub(u i ) + 1

  We consider a smart grid with 1 million users. The utility provides 15 DR projects to users (for example, Real Time Pricing program, Time Of Use Pricing program, Critical Pick Pricing program, ... etc). We assume that users arrival is modeled as a Poisson process with parameter λ (users/months), and given that there are no statistical studies of DR projects membership behavior for the moment, we assume that membership duration in each DR projects follows an Exponential law with parameter µ.

• Simulation Model:


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Caroline Michel

Guillaume Wille

Louis Jolly

Annie Richard

Agnès Burel

Patrick Ollivier

Christophe Mouvet

Jennifer Hellal

Analysis of biofilm-nanoparticles interaction using microscopy (fluorescence, MEB, STEM, MET, EDS)

Keywords: Multispecies natural biofilms, biofilm sampling, sample preparation, cell labelling, Exopolysaccharides labelling, lectins

teaching and research institutions in France or abroad, or from public or private research centers.

Among biofilm's properties, the ability to interact with/catch pollutants can have applications in bioremediation. Here, biofilm interactions with metals (as iron nanoparticles (NanoFer 25S)) was evaluated using various approaches in microscopy. For this, biofilm growth, sampling, labelling and treatment were developed for each type of microscopy to access the surface or inside of the biofilm, biofilm composition, and metal location. Multispecies biofilms were grown on sand or in PVC tubes inoculated with aquifer water spiked with a nutritive solution to enhance denitrification, and then put in contact with nanoparticles. According to the targeted microscopy, biofilms were (i) sampled as flocs or attached biofilm, (ii) submitted to cells (DAPI) and/or lectins (PNA and ConA coupled to FITC or Au nanoparticles) labelling, and (iii) prepared for observation (fixation, cross-section, freezing…). Fluorescent microscopy revealed that nanoparticles were embedded in the biofilm structure as 0.5-5µm size aggregates. SEM observations also showed NP aggregates closed to microorganisms but it was not possible to conclude a potential interaction between nanoparticles and the biological membranes. STEM-in-SEM analysis showed NP aggregates could enter inside the biofilm over a depth of 7-11µm. Moreover, microorganisms were circled by an EPS ring that prevented the direct interaction between NP and membrane. TEM(STEM)/EDS revealed that NP aggregates were co-localized with lectins suggesting a potential role of exopolysaccharides in NP embedding. The combination of several approaches in microscopy is thus a good tool to better understandi and characterize biofilm/pollutant interaction.