Automating interpretations of trustworthiness Submitted by Marc Sel for the degree of Doctor of Philosophy of Royal Holloway, University of London 2021 Declaration I, Marc Sel, hereby declare that this thesis and the work presented in it is entirely my own. Where I have consulted the work of others, this is always clearly stated. Signed . (Marc Sel) Date: 1 To Trijntje. 2 Abstract Digital services have a significant impact on the lives of many individuals and organisations. Trust influences decisions regarding potential service providers, and continues to do so once a service provider has been selected. It is common to refer to the entity that is trusting as the trustor, and to the entity that is trusted as the trustee. There is no globally accepted model to describe trust in the context of digital services, nor to evaluate the trustworthiness of entities. Trust is commonly used in the context of digital services, yet it is overloaded with meaning and difficult to interpret. This thesis presents a novel model to describe and evaluate an entity’s trustworthiness. The model is referred to as the trustworthy ecosystem model. It is based on four building blocks: a data model, a rulebook, trustworthiness evaluation functions and instance data. The data model is expressed in First Order Logic. Rulebooks, which consist of constraints that reflect a particular context for reasoning about trustworthiness, are described using predi- cates. The entity that is evaluating is referred to as the evaluator, and the entity that is evaluated is the evaluation subject. The evaluator corresponds to a potential trustor, and the evaluation subject to a potential trustee. Verifying whether the constraints are satisfied over a set of instance data allows an evaluator to evaluate the trustworthiness of an evaluation subject. For this purpose trustworthiness eval- uation functions are specified. The functions takes as input a rulebook, i.e. a set of constraints, and a set of data. A rulebook contains a mandatory and a discretionary part. The mandatory part describes the constraints that must be satisfied to have the minimal basis for relevant execution of the discretionary rules. The discretionary part allows the evaluator to specify a trustworthi- ness evaluation policy by selecting discretionary constraints. The data represents real world information about the potential trustee and its context. The outcome of the evaluation provides evidence that can be used by the evaluator to decide to interact with the evaluation subject in the relationship of trustor–trustee. To demonstrate the practical feasibility of the proposed framework, a partial implementa- tion is presented. The data model was implemented in OWL, a logic language that was es- tablished by the Worldwide Web Consortium (W3C). The data model was complemented by a data import and transformation mechanism which transforms data from public and authori- tative sources into the data model and stores it in a graph database. A sample rulebook and trustworthiness evaluation function were implemented in the form of SPARQL queries. The implementation is partial because it implements only two particular rulebooks, inspired by the European legislation for trust services, and because it uses a specific set of data sources for its instance data. The approach was validated by implementing the model, importing real world data, per- forming selective evaluations of trustworthiness and comparing their outcome to other ap- 3 proaches such as PKI and the Web of Trust verification. The scientific contribution of the thesis can be summarised as follows: • A thorough investigation of the current academic field on trust and trustworthiness was performed through a literature review, to identify potential improvement points and thus create the basis for the thesis. • An integrated set of requirements for trust, i.e. things which must hold for an entity, or the outcome of an interaction to be regarded as trustworthy, were proposed on the basis of the literature review and the findings of the FutureTrust research project. • Based on these requirements, a new way to logically model providers and consumers of digital services as well as the providers of trust services as participants in a digital ecosys- tem was proposed. It is based on a data model, a rulebook, trustworthiness evaluation functions and instance data. • A partial implementation of the model was performed to validate it, using data from authoritative public sources. 4 Acknowledgements I am very grateful to Professor Chris Mitchell, for being my Supervisor and keeping me on track. His comments were always a wonderful balance in terms of shortness, being to the point and being constructive. He introduced me to the academic world in a fantastic way. Thank you so much, Chris. You were an extraordinary guide. I’m also grateful to Professor Jason Crampton for challenging and supporting me as Advisor. I have really enjoyed every talk, comment and discussion. Thank you so much, Jason. Furthermore, I have to thank Professor Bart Preneel for planting the seed of my curiosity in security and cryptology. And I thank Professor Stephen Marsh for making me feel welcome at various IFIP International Conferences on Trust Management, which provided a challenging breeding ground, and Professor Vijay Varadharajan for interesting discussions. My special thanks go to Detlef and Tina Hühnlein for inviting me to join the inspiring FutureTrust project. Professor Jörg Schwenck as well as Vladimir Mladenov and Juraj Somorovsky are thanked for their friendly collaboration and the exchange of ideas, as well as Herbert Leitold, Thomas Zefferer, Gerald Dißauer, Elif Üstündağ Soykan and Edona Fasllija. I am also indebted to Nikos Loutas for his motivating introduction to the semantic web. Finally I thank Danny De Cock for stimulating discussions. This work was conducted using the Protégé and GraphDB resources. Protégé1 is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health. Ontotext offered free use of their GraphDB database2. 1https://protege.stanford.edu/ 2https://www.ontotext.com/products/graphdb/ 5 Contents 1 Introduction 32 1.1 Motivation . 32 1.1.1 The importance of trust . 32 1.1.2 The meaning of trust . 33 1.1.3 Lack of clarity . 33 1.1.4 Ambiguity . 34 1.1.5 A lack of semantic precision . 35 1.1.6 Lack of a precise trust definition for PKI . 36 1.1.7 Trust and this thesis . 37 1.2 Research challenges . 38 1.2.1 Problem statement . 38 1.2.2 Hypothesis . 38 1.2.3 Research questions . 39 1.3 Research methodology . 39 1.4 Contributions . 40 1.5 Publications . 43 1.5.1 Publications in international conferences . 43 1.5.2 Publications from research projects . 45 1.6 Thesis Outline . 45 1.6.1 Part I Background . 45 1.6.2 Part II Modelling trustworthiness . 46 1.6.3 Part III Using trust and trustworthiness . 47 1.6.4 Appendices . 47 I Background 49 2 Trust, trustworthiness and related concepts 50 2.1 Introduction . 50 6 2.2 Social science approaches . 51 2.2.1 Sociological perspectives . 51 2.2.2 Psychological perspectives . 54 2.2.3 Legal perspectives of on-line transactions . 55 2.3 Formal science approaches . 59 2.3.1 Applying logic to trust relationships . 59 2.3.2 Computational treatments of trust . 61 2.4 Applications of trust . 63 2.4.1 Dependability and trust . 63 2.4.2 Distributed trust . 65 2.4.3 Trust and the Semantic Web . 66 2.4.4 Multidisciplinary approaches to trust . 68 2.5 Summary . 69 3 A structured literature review 73 3.1 Introduction . 73 3.2 Design of the review methodology . 74 3.2.1 Protocol . 74 3.2.2 Background to the approach . 74 3.2.3 Approach used . 75 3.3 Preparation . 76 3.3.1 Scope, purpose and questions . 77 3.3.2 Search terms . 77 3.3.3 Information sources . 80 3.3.4 Selection . 83 3.4 Execution . 84 3.4.1 Longlist criteria . 84 3.4.2 Performing the search . 84 3.4.3 Longlist and shortlist . 85 3.5 Analysis . 85 3.5.1 Surveys and reviews . 85 3.5.2 Articles . 87 3.5.3 Research topics . 104 3.6 Summary . 106 4 Logic and the Semantic Web 108 4.1 Introduction . 108 4.2 Formal logic . 109 7 4.2.1 Sets and logic — basic terminology . 109 4.2.2 First-order Logic . 110 4.3 Semantic web formalisms . 112 4.3.1 Resource Description Framework . 112 4.3.2 SPARQL . 115 4.3.3 Description Logics . 116 4.3.4 Knowledge representation and the Attributive Language AL . 121 4.3.5 The Description Logic SROIQ.D/ . 123 4.3.6 The W3C Web Ontology Language (OWL) . 126 4.4 Summary . 131 II Modelling trustworthiness 133 5 Requirements for trustworthiness 134 5.1 Introduction . 134 5.2 Definitions . 135 5.2.1 Objective and assumptions . 135 5.2.2 Defining trustworthiness . 136 5.3 Requirements from the literature survey . 136 5.3.1 SR1 Semantic definition of trustworthiness . 136 5.3.2 SR2 Reasoning about trustworthiness . 137 5.3.3 SR3 Deciding whether an entity is trustworthy . 137 5.3.4 SR4 Information for reasoning about trustworthiness . 137 5.4 Requirements from FutureTrust . 137 5.4.1 The FutureTrust project . 137 5.4.2 Requirements applicable to all participants . 138 5.4.3 Requirements applicable to participants in a FutureTrust role . 140 5.4.4 Using the FutureTrust trustworthiness requirements . ..