Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans

Mehdy, A K M Nuhil; Ekstrand, Michael D.; Knijnenburg, Bart P.; Mehrpouyan, Hoda

doi:10.1145/3450613.3456829

Cited by 12 publications

(4 citation statements)

References 38 publications

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“…Users perceive privacy differently, e.g., some users tolerate disclosing their gender, while others refuse to do this (Joshaghani et al, 2018). This perceived privacy depends on many factors, e.g., context or situational factors (Knijnenburg and Kobsa, 2013;Mehdy et al, 2021). However, measuring users' perceived privacy is hard and is usually done via questionnaires (Knijnenburg and Kobsa, 2013).…”

Section: Summary and Open Questionsmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Summary and Open Questionsmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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“…Users perceive privacy differently, e.g., some users tolerate disclosing their gender, while others refuse to do this (Joshaghani et al, 2018 ). This perceived privacy depends on many factors, e.g., context or situational factors (Knijnenburg and Kobsa, 2013 ; Mehdy et al, 2021 ). However, measuring users' perceived privacy is hard and is usually done via questionnaires (Knijnenburg and Kobsa, 2013 ).…”

Section: Summary and Open Questionsmentioning

confidence: 99%

Differential privacy in collaborative filtering recommender systems: a review

Müllner,

Lex,

Schedl

et al. 2023

Front. Big Data

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State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' privacy. To alleviate this threat, often, differential privacy is used to protect users' data via adding random noise. This, however, leads to a substantial drop in recommendation quality. Therefore, several approaches aim to improve this trade-off between accuracy and user privacy. In this work, we first overview threats to user privacy in recommender systems, followed by a brief introduction to the differential privacy framework that can protect users' privacy. Subsequently, we review recommendation approaches that apply differential privacy, and we highlight research that improves the trade-off between recommendation quality and user privacy. Finally, we discuss open issues, e.g., considering the relation between privacy and fairness, and the users' different needs for privacy. With this review, we hope to provide other researchers an overview of the ways in which differential privacy has been applied to state-of-the-art collaborative filtering recommender systems.

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“…In one of our earlier works [38], we leveraged the data to measure users' behavioral intention and their situational perception of three constructs: attitude, subjective norm, and perceived behavioral control. These constructs and the path model is inspired from the theory of planned behavior (TPB) [3].…”

Section: Path Model For Privacy Behavior Analysismentioning

confidence: 99%

“…Additionally, individual variations in demographics, personality traits, and decision-making styles as well as their effect on users' privacy-related habits must be studied before developing any behavioral model. Therefore, we work on a dataset from [38] which was obtained by conducting a custom designed survey on Amazon Mechanical Turk 1 (N=401) based on the theory of planned behavior (TPB) to measure the way users' perceptions of privacy factors and intent to disclose information are affected by three situational factors embodied hypothetical scenarios: information type, recipients' role, and trust source.…”

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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Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans

Cited by 12 publications

References 38 publications

Reviews in Recommender Systems: 2022

Reviews in Recommender Systems: 2022

Differential privacy in collaborative filtering recommender systems: a review

Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

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