A dynamic logic for privacy compliance

Boella, Guido; Torre, Leendert van der

doi:10.1007/s10506-011-9114-3

Cited by 11 publications

(13 citation statements)

References 30 publications

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“…In the same way, the law may be seen as a way to constrain agents, or as a mechanism to empower them [62,26,19]. On one hand privacy norms may constrain what agents can know about other agents, but on the other hand it helps the agents to protect their own data and to maintain their privacy [64].…”

Section: Related Workmentioning

confidence: 99%

Towards AI Logic for Social Reasoning

Dong,

Markovich,

van der Torre

2021

Preprint

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Artificial Intelligence (AI) logic formalizes the reasoning of intelligent agents. In this paper, we discuss how an argumentation-based AI logic could be used also to formalize important aspects of social reasoning. Besides reasoning about the knowledge and actions of individual agents, social AI logic can reason also about social dependencies among agents using the rights, obligations and permissions of the agents. We discuss four aspects of social AI logic. First, we discuss how rights represent relations between the obligations and permissions of intelligent agents. Second, we discuss how to argue about the right-to-know, a central issue in the recent discussion of privacy and ethics. Third, we discuss how a wide variety of conflicts among intelligent agents can be identified and (sometimes) resolved by comparing formal arguments. Importantly, to cover a wide range of arguments occurring in daily life, also fallacious arguments can be represented and reasoned about. Fourth, we discuss how to argue about the freedom to act for intelligent agents. Examples from social, legal and ethical reasoning highlight the challenges in developing social AI logic. The discussion of the four challenges leads to a research program for argumentation-based social AI logic, contributing towards the future development of AI logic.

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Section: Related Workmentioning

confidence: 99%

Towards AI Logic for Social Reasoning

Dong,

Markovich,

van der Torre

2021

Preprint

Self Cite

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“…For example, it is a well known database problem that you may be able to find out my identity without asking for it explicitly, for example by asking a very detailed question (all the people who are born in Amsterdam on September 11 1986, who drive a blue Mercedes, and who are married to a person from Paris on November 9, 2009), or by combining a number of queries on a medical database [12]. Aucher, Boella and van der Torre [1] are therefore interested in privacy policies expressed in terms of permitted and forbidden knowledge.…”

Section: Dynamic Epistemic Deontic Logic For Privacy Compliance [1]mentioning

confidence: 99%

“…Aucher, Boella and van der Torre [1] discuss the following scenario of privacy policies. They consider a single agent (Sender) communicating information from a knowledge base to another agent (Recipient), with the effect that the Recipient knows the information.…”

Section: Dynamic Epistemic Deontic Logic For Privacy Compliance [1]mentioning

confidence: 99%

Logics for Security and Privacy

Torre

2012

Data and Applications Security and Privacy XXVI

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“…Therefore, it is important than ever before to develop and provide suitable tools and algorithms to the users so that they can define, manage, and make the best use of their privacy preferences with ease. Existing methodologies and protocols intend to tackle this problem by employing technique such as access control policies [44,46], machine readable privacy policy languages [5,16], formal methods [6,11], machine learning [15,39,48], etc. However, most of the works attempt to frame the problem from a request's perspective which lack the crucial involvement of the information owner, resulting in limited or no control of policy adjustment.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

Mehdy

Mehrpouyan

2021

Proceedings of the 16th International Conference on Availability, Reliability and Security

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In order to create user-centric and personalized privacy management tools, the underlying models must account for individual users' privacy expectations, preferences, and their ability to control their information sharing activities. Existing studies of users' privacy behavior modeling attempt to frame the problem from a request's perspective, which lack the crucial involvement of the information owner, resulting in limited or no control of policy management. Moreover, very few of them take into the consideration the aspect of correctness, explainability, usability, and acceptance of the methodologies for each user of the system. In this paper, we present a methodology to formally model, validate, and verify personalized privacy disclosure behavior based on the analysis of the user's situational decision-making process. We use a model checking tool named UPPAAL to represent users' self-reported privacy disclosure behavior by an extended form of finite state automata (FSA), and perform reachability analysis for the verification of privacy properties through computation tree logic (CTL) formulas. We also describe the practical use cases of the methodology depicting the potential of formal technique towards the design and development of user-centric behavioral modeling. This paper, through extensive amounts of experimental outcomes, contributes several insights to the area of formal methods and user-tailored privacy behavior modeling. CCS CONCEPTS• Security and privacy → Formal methods and theory of security.

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A dynamic logic for privacy compliance

Cited by 11 publications

References 30 publications

Towards AI Logic for Social Reasoning

Towards AI Logic for Social Reasoning

Logics for Security and Privacy

Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

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