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Model-Based Privacy by Design

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Model-Based Privacy by Design Model-Based Privacy by Design by Amirshayan Ahmadian, M.Sc. Approved Dissertation thesis for the partial fulfilment of the requirements for a Doctor of Natural Sciences (Dr. rer. nat.) Fachbereich 4: Informatik Universität Koblenz-Landau Chair of PhD Board: Prof. Dr. Maria A. Wimmer Chair of PhD Commission: Prof. Dr. Harald F.O. Von Korflesch Examiner and Supervisor: Prof. Dr. Jan Jürjens Further Examiner: Prof. Dr. Patrick Delfmann Co-Supervisor: Dr. Daniel Strüber Date of the doctoral viva: January 22, 2020 iii Dedicated to my beloved parents. v Abstract Nowadays, almost any IT system involves personal data processing. In such sys- tems, many privacy risks arise when privacy concerns are not properly addressed from the early phases of the system design. The General Data Protection Regu- lation (GDPR) prescribes the Privacy by Design (PbD) principle. As its core, PbD obliges protecting personal data from the onset of the system development, by ef- fectively integrating appropriate privacy controls into the design. To operationalize the concept of PbD, a set of challenges emerges: First, we need a basis to define pri- vacy concerns. Without such a basis, we are not able to verify whether personal data processing is authorized. Second, we need to identify where precisely in a system, the controls have to be applied. This calls for system analysis concerning privacy concerns. Third, with a view to selecting and integrating appropriate con- trols, based on the results of system analysis, a mechanism to identify the privacy risks is required. Mitigating privacy risks is at the core of the PbD principle. Fourth, choosing and integrating appropriate controls into a system are complex tasks that besides risks, have to consider potential interrelations among privacy controls and the costs of the controls. This thesis introduces a model-based privacy by design methodology to handle the above challenges. Our methodology relies on a precise definition of privacy con- cerns and comprises three sub-methodologies: model-based privacy analysis, model- based privacy impact assessment and privacy-enhanced system design modeling. First, we introduce a definition of privacy preferences, which provides a basis to spec- ify privacy concerns and to verify whether personal data processing is authorized. Second, we present a model-based methodology to analyze a system model. The results of this analysis denote a set of privacy design violations. Third, taking into account the results of privacy analysis, we introduce a model-based privacy im- pact assessment methodology to identify concrete privacy risks in a system model. Fourth, concerning the risks, and taking into account the interrelations and the costs of the controls, we propose a methodology to select appropriate controls and integrate them into a system design. Using various practical case studies, we evalu- ate our concepts, showing a promising outlook on the applicability of our method- ology in real-world settings. vi Abstract vii Zusammenfassung In IT-Systemen treten viele Datenschutzrisiken auf, wenn Datenschutzbedenken in den frühen Phasen des Entwicklungsprozesses nicht angemessen berücksichtigt werden. Die Datenschutz-Grundverordnung (DSGVO) schreibt das Prinzip des Datenschutz durch Technikgestaltung (PbD) vor. PbD erfordert den Schutz person- enbezogener Daten von Beginn des Entwicklungsprozesses an, durch das frühzei- tige Integrieren geeigneter Maßnahmen. Bei der Realisierung von PbD ergeben sich nachfolgende Herausforderungen: Erstens benötigen wir eine präzise Defi- nition von Datenschutzbedenken. Zweitens müssen wir herausfinden, wo genau in einem System die Maßnahmen angewendet werden müssen. Drittens ist zur Auswahl geeigneter Maßnahmen, ein Mechanismus zur Ermittlung der Daten- schutzrisiken erforderlich. Viertens müssen bei der Auswahl und Integration geeigneter Maßnahmen, neben den Risiken, die Abhängigkeiten zwischen Maß- nahmen und die Kosten der Maßnahmen berücksichtigt werden. Diese Dissertation führt eine modellbasierte Methodik ein, um die oben genannten Herausforderungen zu bewältigen und PbD zu operationalisieren. Unsere Methodik basiert auf einer präzisen Definition von Datenschutzbe- denken und umfasst drei Untermethodiken: modellbasierte Datenschutzanalyse, mo- dellbasierte Datenschutz-Folgenabschätzung und datenschutzfreundliche Systemmodel- lierung. Zunächst führen wir eine Definition für Datenschutzpräferenzen ein, an- hand derer die Datenschutzbedenken präzisiert werden können und überprüft werden kann, ob die Verarbeitung personenbezogener Daten autorisiert ist. Zwei- tens präsentieren wir eine modellbasierte Methodik zur Analyse eines Systemmo- dells. Die Ergebnisse dieser Analyse ergeben die Menge der Verstöße gegen die Datenschutzpräferenzen in einem Systemmodell. Drittens führen wir eine mo- dellbasierte Methode zur Datenschutz-folgenabschätzung ein, um konkrete Daten- schutzrisiken in einem Systemmodell zu identifizieren. Viertens schlagen wir in Bezug auf die Risiken, Abhängigkeiten zwischen Maßnahmen und Kosten der Maßnahmen, eine Methodik vor, um geeignete Maßnahmen auszuwählen und in ein Systemdesign zu integrieren. In einer Reihe von realistischen Fallstudien be- werten wir unsere Konzepte und geben einen vielversprechenden Ausblick auf die Anwendbarkeit unserer Methodik in der Praxis. ix Contents Abstract v Zusammenfassung vii Abbreviations xxi Acknowledgements xxiii 1 Introduction 1 1.1 Challenges and Research Directions . 4 1.1.1 Privacy Preferences . 5 1.1.2 Privacy Analysis . 5 1.1.3 Privacy Impact Assessment . 6 1.1.4 Privacy Enhancement . 6 1.2 Contributions . 7 1.3 Methodology . 9 1.4 Thesis Outline . 11 1.5 How to Read this PhD Thesis . 12 1.6 Preliminary Publications . 13 2 Model-Based Privacy by Design: An Overview of the Methodology 15 2.1 The Common Terms in this Thesis . 15 2.2 Running Example . 16 2.3 Walk-Through: Model-Based Privacy by Design . 20 2.3.1 Privacy Preferences . 20 2.3.2 Model-Based Privacy Analysis . 22 2.3.3 Model-Based Privacy Impact Assessment . 22 2.3.4 Privacy-Enhanced System Design Modeling . 23 3 Privacy Preferences: A Foundation 25 3.1 Introduction . 26 x Contents 3.2 Background . 27 3.2.1 The Four Key Elements of Privacy . 27 3.2.2 A Background on the Theory of Sets and Lattices . 28 3.2.3 Privacy Level Agreements . 30 3.3 Privacy Preferences . 31 3.4 Formalized Privacy Level Agreements . 43 3.4.1 A Brief Description of the Differences Between the GDPR and Directive 95/46/EC . 43 3.4.2 The PLA Metamodel . 44 3.5 Discussion and Limitations . 46 3.5.1 Revisiting the Research Questions . 46 3.5.2 Limitations . 47 3.6 Related Work . 48 3.7 Preliminary Conclusion . 51 4 Model-Based Privacy Analysis 53 4.1 Introduction . 54 4.2 Background . 56 4.2.1 Unified Modeling Language (UML) . 56 4.2.1.1 Class Diagram . 57 4.2.1.2 Activity Diagram . 57 4.2.1.3 Deployment Diagram . 58 4.2.1.4 State Machines . 59 4.2.2 UML Profile . 59 4.2.3 Model-Based Security Analysis Using UMLsec . 60 4.2.3.1 Secure Links . 60 4.2.3.2 Secure Dependency . 62 4.3 Model-Based Privacy Analysis in Industrial Ecosystems . 64 4.3.1 The Modular Privacy Analysis . 64 4.3.2 Privacy Analysis Based on the Four Fundamental Privacy El- ements . 66 4.3.3 UML Privacy Extension . 68 4.3.3.1 Privacy Profile . 69 4.3.3.2 rabac Profile . 71 4.3.4 The Privacy Checks . 73 4.3.5 Applying the Privacy Checks: Example . 80 4.4 Case Studies and a Survey . 82 4.4.1 Case Studies . 83 4.4.2 Investigating the Required Support to Carry out the Pro- posed Methodology . 85 4.4.2.1 A Survey on System Modeling within the VisiOn Project . 85 Contents xi 4.4.2.2 Our Observation and Conclusion . 87 4.5 Discussion and Limitations . 89 4.6 Related Work . 91 4.7 Preliminary Conclusion . 93 5 Supporting Privacy Impact Assessment by Model-Based Privacy Analysis 95 5.1 Introduction . 96 5.2 Background . 97 5.2.1 Risk, Threat, and Risk Analysis . 97 5.2.2 Privacy Impact Assessment . 98 5.2.3 Privacy Targets . 99 5.2.4 Privacy Threats . 99 5.3 Model-Based Privacy Impact Assessment . 100 5.3.1 Systematic Specification of System and its Privacy-Critical Parts100 5.3.2 Model-Based Privacy and Security Analysis . 102 5.3.3 Identification of Harmful Activities and Threats . 103 5.3.4 Impact Assessment . 106 5.3.5 Identification of Appropriate Controls . 113 5.3.6 Privacy Impact Assessment Report . 114 5.4 Case Studies and Evaluation . 115 5.4.1 Case Studies . 115 5.4.2 Comparative Evaluation . 116 5.5 Discussion and Limitations . 118 5.6 Related Work . 120 5.7 Preliminary Conclusion . 123 6 Privacy-Enhanced System Design Modeling Based on Privacy Features 125 6.1 Introduction . 126 6.2 Background . 128 6.2.1 Privacy Design Strategies, Patterns, and Privacy-Enhancing Technologies . 128 6.2.2 Function Point Analysis (FPA) . 128 6.2.3 Reusable Aspect Models (RAMs) . 129 6.3 Running Example . 130 6.4 Privacy-Enhanced System Design Modeling . 132 6.4.1 The Privacy Design Strategies Feature Model . 133 6.4.2 Model-Based Cost Estimation . 136 6.4.3 Model-Based Privacy Enhancement . 138 6.4.3.1 UML Profile for Privacy Enhancement. 138 6.4.3.2 Using and Extending RAMs for Privacy by Design. 141 6.5 Case Studies and Evaluation . 143 6.6 Discussion and Limitations . 144 xii Contents 6.7 Related Work . 146 6.8 Preliminary Conclusion . 147 7 Tool Support 149 7.1 Introduction . 149 7.2 Model-Based Privacy Analysis with CARiSMA in the Context of the VPP . 150 7.2.1 Overview and New Features . 152 7.2.2 Security and Privacy Analysis within the VisiOn Privacy Plat- form . 153 7.2.2.1 The Architecture of VPP . 153 7.2.2.2 System Model Analysis Using CARiSMA . 155 7.2.2.3 RABAC . 157.
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