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This content will become publicly available on August 17, 2022

Title: Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach
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 more » user-tailored privacy behavior modeling. « less
Authors:
;
Award ID(s):
1657774
Publication Date:
NSF-PAR ID:
10322476
Journal Name:
ARES 2021: The 16th International Conference on Availability, Reliability and Security
Sponsoring Org:
National Science Foundation
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