Faiza LOUKIL Towards a new data privacy-based approach for IoT---------------------------------------------------------------------------------------------------------------------------------------------- LOUKIL Faiza. Towards a new data privacy-based approach for IoT, sous la direction de Chirine Ghedira-Guegan, Université Jean Moulin (Lyon 3) et Aïcha Nabila Benharkat, INSA (Lyon)Thèse soutenue le 15/10/2019.Disponible sur : http://www.theses.fr/2019LYSE3044 ----------------------------------------------------------------------------------------------------------------------------------------------- Document diffusé sous le contrat Creative Commons « Attribution – Pas d’utilisation commerciale - Pas de modification »Vous êtes libre de le reproduire, de le distribuer et de le communiquer au public à condition d’en mentionner le nom de l’auteur et de ne pas le modifier, le transformer, l’adapter ni l’utiliser à des fins commerciales. N°d’ordre NNT : 2019LYSE3044 THÈSE de DOCTORAT DE L’UNIVERSITÉ DE LYON opérée au sein de L’Université Jean Moulin Lyon 3 Ecole Doctorale N° 512 ED en Informatique et Mathématiques (InfoMaths) Discipline de doctorat : Informatique Soutenue publiquement le 15/10/2019, par : Faiza LOUKIL Towards a new data privacy-based approach for IoT Devant le jury composé de : Genoveva VARGAS SOLAR, Chargée de Recherche HDR, CNRS, Laboratoire LIG Harald KOSCH, Professeur des universités, Université Passau-Allemagne Rapporteur Michael MRISSA, Professeur des universités, InnoRenew CoE Université de Primorska, Slovenie Rapporteur Benjamin NGUYEN, Professeur des universités, INSA Centre Val de Loire, Bourges Chirine GHEDIRA-GUEGAN, Professeur des universités, IAELyon Université Lyon3 Directrice de thèse Aïcha-Nabila BENHARKAT, Maître de Conférences, INSA de Lyon Co-directrice de thèse AcknowledgmentsAt the beginning of this dissertation and the end of this journey, I would like to thank all thosewho helped me to complete this work. Firstly, I would like to thank my academic supervisor,Mrs. Chirine GHEDIRA-GUEGAN for the numerous scientific discussions, the insightful inputson my work, and her guidance all along my thesis. I would also like to express my gratitude toMrs. Aïcha-Nabila BENHARKAT for her interesting comments, advices, and reflections. Bothhave provided me with valuable academic suggestions, relevant research ideas, and administra-tive supports during my research.I would also like to thank the members of the jury who evaluated my work: Mr. Harald KOSCHand Mr. Michael MRISSA for reviewing my dissertation, as well as Mrs. Genoveva VARGASSOLAR and Mr. Benjamin NGUYEN for being part of my jury and their interest in my work.I would like to express my gratitude to Mrs. Khouloud BOUKADI for her appreciate collab-oration in my research publications in academic conferences over the past three years and herencouragements when they were the most needed.I would also like to thank my fellow PhD students in SOC team for all the passionate dis-cussions, the not scientific support, and the shared trips.Finally, I thank my lovely family in Sfax for their constant support. Thanks to their encour-agement, comprehension, and support, I keep moving forward to achieve my objective andcomplete this amazing journey.iiiAbstractThe Internet of Things (IoT) connects and shares data collected from smart devices in severaldomains, such as smart home, smart grid, and healthcare. According to Cisco, the number ofconnected devices is expected to reach 500 Billion by 2030. Five hundred zettabytes of datawill be produced by tremendous machines and devices. Usually, these collected data are verysensitive and include metadata, such as location, time, and context. Their analysis allows thecollector to deduce personal habits, behaviors and preferences of individuals. Besides, thesecollected data require the collaboration of several parties to be analyzed. Thus, due to the highlevel of IoT data sensitivity and lack of trust on the involved parties in the IoT environment, thecollected data by different IoT devices should not be shared with each other, without enforcingdata owner privacy. In fact, IoT data privacy has become a severe challenge nowadays, especiallywith the increasing legislation pressure.Our research focused on three complementary issues, mainly (i) the definition of a semanticlayer designing the privacy requirements in the IoT domain, (ii) the IoT device monitoring andthe enforcement of a privacy policy that matches both the data owner’s privacy preferencesand the data consumer’s terms of service, and (iii) the establishment of an end-to-end privacy-preserving solution for IoT data in a decentralized architecture while eliminating the need totrust any involved IoT parties.To address these issues, our work contributes to three axes. First, we proposed a new Eu-ropean Legal compliant ontology for supporting preserving IoT PrivacY, called LIoPY that de-scribes the IoT environment and the privacy requirements defined by privacy legislation andstandards. Then, we defined a reasoning process whose goal is generating a privacy policy bymatching between the data owner’s privacy preferences and the data consumer’s terms of ser-vice. This privacy policy specifies how the data will be handled once shared with a specific dataconsumer. In order to ensure this privacy policy enforcement, we introduced an IoT data privacy-preserving framework, called PrivBlockchain, in the second research axis. PrivBlockchain is anend-to-end privacy-preserving framework that involves several parties in the IoT environmentfor preserving IoT data privacy during the phases of collection, transmission, storage, and pro-cessing. The proposed framework relied on, on the one hand, the blockchain technology, thussupporting a decentralized architecture while eliminating the need to trust any involved IoTparties and, on the other hand, the smart contracts, thus supporting a machine-readable andself-enforcing privacy policy whose goal is to preserve the privacy during the whole data life-cycle, covering the collection, transmission, storage and processing phases. Finally, in the thirdaxis, we designed and implemented the proposal in order to prove its feasibility and analyzeits performances.v0关键词：隐私，物联网（IoT），本体和推理，区块链技术。vii0摘要0物联网设备在智能家居、智能电力分配网络和健康等领域收集和共享数据。根据思科的数据，到2030年，物联网设备数量预计将达到5000亿台，产生的数据量约为500个泽塔字节。然而，这些收集到的数据通常非常丰富，经常包含位置、时间信息和上下文等元数据，从而很容易推断出个人的习惯、行为和偏好。此外，分析这些收集到的数据需要多个参与者的合作。因此，由于数据的高度敏感性和参与方之间的信任缺乏，在不尊重数据所有者的隐私的情况下，这些数据不应该被共享。事实上，保护物联网设备数据的隐私已经成为一个重大挑战，尤其是在法律压力不断增加的情况下。我们的研究工作集中在三个互补的问题上，即隐私保护要求建模问题、物联网设备监控问题以及符合数据所有者偏好和数据使用者条件的共同政策保证问题，以及在去中心化架构中保护这些设备生成的数据的隐私问题，该架构消除了对物联网设备网络中参与方的信任需求。为了解决这些问题，我们首先提出了一个名为LIoPY的本体，该本体对元数据进行建模，并与隐私保护的标准和法律相适应。然后，为了在隐私保护要求和数据使用者之间进行语义对齐，我们通过层次关系和语义推理规则扩展了本体，生成了一个共同的隐私保护政策。该政策描述了与给定消费者共享数据后如何处理数据。为了确保遵守这一共同政策，我们引入了PrivBlockchain框架，这是一个涉及物联网设备网络中所有参与方的框架，用于保护从这些设备收集的数据在收集、传输、存储和使用或分析阶段的隐私。所提出的框架一方面基于区块链技术，支持去中心化架构，同时消除了对物联网设备网络中参与方的信任需求；另一方面，基于“智能”合约，支持自动应用和机器可读的政策。其作用是保护从物联网设备收集的数据在收集、传输、存储和分析阶段的隐私。最后，我们通过制作和实施一个原型来验证我们的提议，以证明其可行性并分析其性能。0关键词：隐私保护，物联网，本体和推理，区块链技术。List of figuresxiiiList of tablesxvList of algorithmsxviiIntroduction11Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12Research Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33Contributions summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .54Thesis approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75Organization of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8Chapter 1Related Work91.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91.2Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101.2.1Privacy legislation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101.2.2Privacy design strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . .131.2.3Three-layered privacy model . . . . . . . . . . . . . . . . . . . . . . . . . . .141.2.4Privacy-preserving architectures . . . . . . . . . . . . . . . . . . . . . . . . .151.2.5Privacy-preserving mechanisms . . . . . . . . . . . . . . . . . . . . . . . . .151.2.6Related privacy-preserving approaches in several domains. . . . . . . . .181.3Privacy-preserving approaches in the IoT domain . . . . . . . . . . . . . . . . . . .211.3.1Centralized-based approaches. . . . . . . . . . . . . . . . . . . . . . . . . .211.3.2Distributed-based approaches. . . . . . . . . . . . . . . . . . . . . . . . . .241.3.3Trusted Third-Party-based approaches. . . . . . . . . . . . . . . . . . . . .271.4Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .291.5Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32Chapter 2LIoPY: European Legal compliant ontology for supporting preserving IoT Pri-vacY332.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .332.2Existing IoT and privacy ontologies. . . . . . . . . . . . . . . . . . . . . . . . . . .342.3Overview of the European Legal compliant ontology for supporting preservingIoT PrivacY (LIoPY) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .392.3.1Building LIoPY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .392.3.2LIoPY’s Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .482.4Privacy Preferences Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50ix0目录0摘要 v0简历 viiCONTENTS2.4.1Collected-data-centric privacy-preserving perspective. . . . . . . . . . . .512.4.2Shared-data-centric privacy-preserving perspective . . . . . . . . . . . . . .522.5Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67Chapter 3Semantic Rule Manager: Reasoning process validation693.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .693.2Reasoning process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .703.2.1Privacy Attribute Matching algorithm definition . . . . . . . . . . . . . . . . .703.2.2Privacy Attribute Matching algorithm implementation . . . . . . . . . . . . .733.2.3Privacy Attribute Matching algorithm validation: Semantic Rule Manager.773.3Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .793.3.1Motivating scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .793.3.2Experimental environment. . . . . . . . . . . . . . . . . . . . . . . . . . . .793.3.3Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .803.4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84Chapter 4PrivBlockchain: Blockchain-based IoT data privacy-preserving framework854.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .864.2Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .864.2.1Blockchain technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .864.2.2Homomorphic encryption technology . . . . . . . . . . . . . . . . . . . . . .874.3Design goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .894.4PrivBlockchain: IoT data privacy-preserving framework . . . . . . . . . . . . . . .904.4.1PrivBlockchain overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . .904.4.2PrivBlockchain core components . . . . . . . . . . . . . . . . . . . . . . . . .924.4.3PrivBlockchain modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .974.5Blockchain-based IoT device management module . . . . . . . . . . . . . . . . . . .984.5.1Smart contract description. . . . . . . . . . . . . . . . . . . . . . . . . . . .984.5.2Privacy permission setting adding process . . . . . . . . . . . . . . . . . . .994.6Blockchain-based IoT data sharing module . . . . . . . . . . . . . . . . . . . . . . .1054.6.1Smart contract description. . . . . . . . . . . . . . . . . . . . . . . . . . . .1064.6.2IoT data sharing process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1064.7Homomorphic encryption-based IoT data aggregation module. . . . . . . . . . .1114.7.1Smart contract description. . . . . . . . . . . . . . . . . . . . . . . . . . . .1124.7.2Privacy policy generation process . . . . . . . . . . . . . . . . . . . . . . . .1124.7.3IoT data aggregation process . . . . . . . . . . . . . . . . . . . . . . . . . . .1144.8Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .118Chapter 5Evaluation and Analysis1195.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1195.2Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1205.2.1Motivating scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1205.2.2Experimental environment. . . . . . . . . . . . . . . . . . . . . . . . . . . .1215.3Blockchain-based smart home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1215.3.1IoT device management use case . . . . . . . . . . . . . . . . . . . . . . . . .1215.3.2Security and privacy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .125x05 . 3 . 3 性能评估 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12605 . 4 基于区块链的医疗系统 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12805 . 4 . 1 物联网数据共享用例 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12805 . 4 . 2 安全性和隐私性分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13205 . 4 . 3 性能评估 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13305 . 5 基于区块链的智能电网 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13605 . 5 . 1 物联网数据聚合用例 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13605 . 5 . 2 安全性和隐私性分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13805 . 5 . 3 性能评估 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13905 . 6 性能比较分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142xi0目录05 . 7 总结：隐私设计策略的合规性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1470结论和未来工作 149 1 背景和目标 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14902 提出的贡献 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15003 本研究的局限性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15104 未来工作 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1520参考文献 1600附录A 使用OntoMetrics平台对LIoPY进行评估 1610附录B 基于智能合约的异常行为检测结果 1650图目录01 论文方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702 . 1 SOUPA概述 [Chen等, 2004 ] . . . . . . . . . . . . . . . . . . . . . . . .
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