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OPP-Iot an Ontology-Based Privacy Preservation Ap- Proach for the Internet of Things

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OPP-Iot an Ontology-Based Privacy Preservation Ap- Proach for the Internet of Things THÈSE Pour obtenir le grade de DOCTEUR DE la Communauté UNIVERSITÉ GRENOBLE ALPES Spécialité : Informatique Arrêté ministériel : 27 Janvier 2017 Présentée par Thiago Moreira da Costa Thèse dirigée par Hervé Martin et codirigée par Nazim Agoulmine préparée au sein LIG - Laboratoire d’Informatique de Grenoble et de l’EDMSTII - l’Ecole Doctorale Mathématiques, Sciences et Tech- nologies de l’Information, Informatique OPP-IoT An ontology-based privacy preservation ap- proach for the Internet of Things Thèse soutenue publiquement le 27 Janvier 2017, devant le jury composé de : M, Didier DONZES Docteur, Pr. à l’Université Grenoble Alpes, Président MME, Karine ZEITOUNI Docteur, Pr. à l’Université de Versailles-Saint-Quentin-en-Yvelines, Rapporteur MME, Maryline LAURENT Docteur, Pr. à l’Université Telecom SudParis, Rapporteur M, Reinaldo BRAGA Docteur, Pr. à l’Institut Fédéral du Ceará, Examinateur Thiago Moreira da Costa: OPP-IoT, An ontology-based privacy preservation approach for the Internet of Things, © 27 Janvier 2017 To my beloved parents... ABSTRACT The spread of pervasive computing through the Internet of Things (IoT) represents a challenge for privacy preservation. Privacy threats are directly related to the capacity of the IoT sensing to track indi- viduals in almost every situation of their lives. Allied to that, data mining techniques have evolved and been used to extract a myriad of personal information from sensor data stream. This trust model relies on the trustworthiness of the data consumer who should infer only intended information. However, this model exposes personal in- formation to privacy adversary. In order to provide a privacy preser- vation for the IoT, we propose a privacy-aware virtual sensor model that enforces privacy policy in the IoT sensing. This mechanism in- termediates physical sensors and data consumers. As a consequence, we are able to optimize the use of privacy preserving techniques by applying them selectively according to virtual sensor inference inten- tions, while preventing malicious virtual sensors to execute or get di- rect access to raw sensor data. In addition, we propose an ontology to classify personal information based on the Behavior Computing, facil- itating privacy policy definition and information classification based on the behavioral contexts. v RÉSUMÉ La vulgarisation de l’informatique omniprésente à travers l’internet des objets (IdO) représente un défi pour la préservation de la vie pri- vée et la confidentialité des individus. Les menaces contre la confi- dentialité sont directement liées à la capacité de détection de capteur dans l’IdO en suivant les individus dans presque toutes les situations de leur vie. Alliée à cela, les techniques d’exploration de données ont évolué et ont été utilisées pour extraire une multitude d’informations personnelles à partir de données du flux des données des capteurs. Ce modèle de confiance repose sur la fiabilité du consommateur de données pour extraire uniquement des informations accordées. Ce- pendant, ce modèle permet l’exposition d’informations personnelles à des adversaires de la vie privée. Afin de fournir un mécanisme pour préserver la confidentialité dans l’IdO, nous proposons un mo- dèle de capteur virtuel qui renforce une politique de confidentialité dans le flux des données des capteurs. Ce mécanisme intermédiaire se met en place entre les capteurs physiques et les consommateurs de données. En conséquence, nous sommes en mesure d’optimiser l’utilisation des techniques de preservation de confidentialité, telles qu’anonymisation, en les appliquant de manière sélective selon les intentions d’inférence des capteurs virtuelles, tout en empêchant les capteurs virtuels malveillants d’exécuter ou d’obtenir un accès direct aux données brutes des capteurs physiques. En outre, nous propo- sons une ontologie pour classer les informations personnelles basées sur la science du comportement (Behavior Computing), ce qui facilite la définition de la politique de confidentialité et à la classification de l’information en fonction des contextes comportementaux. vi PUBLICATIONS — Thiago Moreira da Costa, Hervé Martin, and Nazim Agoul- mine. "Privacy-Aware personal Information Discovery model based on the cloud." Network Operations and Management Symposium (LANOMS), 2015 Latin American. IEEE, 2015. Alagoas, Brazil. — Emmanuel Coutinho, Macerlo Santos, Stenio Fernandes, José Neuman de Souza, Thiago Moreira da Costa, Elie Rachkidi, Nazim Agoulmine, and Javier Baliosian, "Research Opportuni- ties in an Intercloud Environment Using MOSt in SLA4CLOUD Project," in International Workshop on ADVANCEs in ICT In- frastructures and Services. Fortaleza, Brazil. — Thiago Moreira da Costa, Elie Rachkidi and Leonardo M. Gar- dini, César Moura, Luiz O. M. Andrade and Mauro Oliveira "An Architecture for LARIISA: An Intelligent System for Deci- sion Making and Service Provision in Health Care," 4th Inter- national Workshop on ADVANCEs in ICT Infrastructures and Services, 2015. Fortaleza, Brazil. — Thiago Moreira da Costa, Elie Rachkidi, Nazim Agoulmine, and Herve Martin, ’An experiment on deploying a privacy- aware sensing as a service in the sensor cloud,’ in IEEE AD- VANCESs in ICT, 2017. Paris, France vii ACKNOWLEDGMENTS Foremost, I would like to express my sincere gratitude to my su- pervisor Prof. Hervé Martin and my advisor Prof. Nazim Agoulmine for the continuous support of my research, and for their enthusiasm, patience, and guidance. In particular, I would like to thank Prof. Hervé Martin for accepting me in the Ph.D. candidacy and being a rock to overcome obstacles that I found before and during this jour- ney. I would like to thank Nazim Agoulmine, as well, for accepting the challenge of co-orientating my work, for inviting me to work with his team at the Université d’Évry, and for being my weekly contact. I would also like to thank the rest of my thesis committee: Prof. Karine ZEITOUNI, prof. Maryline LAURENT, prof. Reinaldo BRAGA, and prof. Didier DONZES, for their insightful review comments, clarifying remarks, and highly relevant questions. I thank CAPES and the MEC (the Brazilian Ministry of Education and Communication) for the financial support, the LIG (Laboratoire d’Informatique de Grenoble), STEAMER team, Paule-Annick, Philipe for having me and supporting my research. Thank you, Frank, Elie, and the IBISC team for having me at the laboratory, and contributing to the quality of my work. In particular, my labmate Elie who joined sleepless nights to meet several 12PM ESTs (UTC/GMT - 5 hours). My sincere thanks also go to my family who supported me with all possible manners, giving me the strength to step forward when my mind was tired and when troubled water was under the bridge. I thank my dad, mom, brother, and aunt Janete. My sincere gratitude to Simon, for being my family in Paris and supporting me in every sit- uation; to Humberto for all support, conversation, and fraternity, and limitless affinity. Without all your love, I am not sure if I would be able to accomplish this alone. I want to thank Rafael for supporting me and believing in my potential since the beginning. My thanks also go to beloved friends who made life easier during these 3 years: Fer- nando, David, James, Rolando, Joseane, Fábio, Fran´cois,Marc-Olivier, Alexis, Ana, Peter, Pierre, and Nicolas, for each moment of conversa- tion, nights out, trips, and dinners. Reinaldo and Carina, thank you both for believing in my potential since 2008! and pushing me to go that extra mile to achieve my dreams. And above all, I thank you, coffee! ix CONTENTS 1 1 1.1 Context . 1 1.2 Privacy . 2 1.3 Personal Information and Behavior . 3 1.4 Motivation and Problem Statement . 4 1.5 Objectives and Approach . 6 1.5.1 Privacy Model . 7 1.5.2 Ontology for Personal Information Classifica- tion on the Sensor Web . 8 1.5.3 Privay-aware Virtual Sensor Model: A Privacy- by-Policy Mechanism . 9 1.5.4 Privacy-aware Sensing as a Service Testbed . 10 1.6 Thesis Structure . 11 i 13 2 15 2.1 Internet of Things . 16 2.1.1 IoT Enabling Technologies . 16 2.1.2 IoT Architectures and Middlewares . 19 2.1.3 The Cloud Computing Service Model . 21 2.2 Privacy Engineering . 24 2.3 IoT Perspectives . 28 2.3.1 Device-centric Perspective . 29 2.3.2 Data-centric Perspective . 32 2.3.3 Human-centric Perspective . 34 2.4 Enabling Technologies for Privacy Preservation in the IoT .............................. 37 2.4.1 Analysis Criteria . 37 2.5 Conclusion . 43 3 , - 45 3.1 Semantic Web technology . 46 3.1.1 Resource Description Framework . 46 3.1.2 RDF Schema and Ontology Web Language . 48 3.1.3 SPARQL Query Language and Protocol . 50 3.1.4 OWL Profiles . 51 3.1.5 Ontology levels . 52 3.1.6 Semantic Web Rule Language . 53 3.2 Semantic Sensor Network . 54 3.3 KDDM and the Meta-Mining . 56 3.3.1 KDDM process . 56 3.3.2 Meta-Mining . 58 3.4 Behavioral Computing . 66 3.5 Related Works . 70 3.6 Conclusion . 71 4 73 xi xii 4.1 Privacy-Preserving Data Mining Techniques . 73 4.1.1 Privacy-Preserving Data Mining for Data Streams 77 4.2 Access Control Mechanisms . 78 4.3 Conclusion . 85 ii 87 5 : 89 5.1 The rationale . 90 5.2 Goal, scope, and competencies . 91 5.3 Ontology Design . 92 5.4 OPIS - Ontology for Personal Information on Sensor web 97 5.5 Behavioral Model for Personal Information . 98 5.5.1 Ontologies for Personal Information and Behav- ioral Recognition . 99 5.6 The Semantic Perception Paradigm . 102 5.6.1 Information Abstraction and the Semantic Per- ception . 102 5.6.2 Meta-Mining for Semantic Perception and Vir- tual Sensors . 103 5.7 The Personal Information Layer . 104 5.8 The Semantic Perception Layer . 109 5.8.1 Semantic Perception Process Specification . 109 5.8.2 Virtual Sensor Implementation and Execution .
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