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1. Exploring Use of Explanative Illustrations to Communicate Differential Privacy Models

   https://doi.org/10.1177/21695067231195006  

   Xiong, Aiping; Wu, Chuhao; Wang, Tianhao; Proctor, Robert W.; Blocki, Jeremiah; Li, Ninghui; Jha, Somesh (September 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Proper communication is key to the adoption and implementation of differential privacy (DP). In this work, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals’ privacy and preserve group utility. Following a pilot survey and an interview, we conducted an online experiment ( N = 300) exploring participants’ comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. We obtained empirical evidence showing participants’ acceptance of the Shuffler DP model for data privacy protection. We discuss the implications of our findings. 

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2. Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

   https://doi.org/10.1145/3465481.3470102  

   Mehdy, A K; Mehrpouyan, Hoda (August 2021, ARES 2021: The 16th International Conference on Availability, Reliability and Security)

   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. 

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3. Security and Privacy of Integrated Sensing and Communication Systems

   https://doi.org/10.1145/3733965.3733974  

   Zeng, Kai (June 2025, ACM)

   Integrated sensing and communication (ISAC) is considered an emerging technology for 6th-generation (6G) wireless and mobile networks. It is expected to enable a wide variety of vertical applications, ranging from unmanned aerial vehicles (UAVs) detection for critical infrastructure protection to physiological sensing for mobile healthcare. Despite its significant socioeconomic benefits, ISAC technology also raises unique challenges in system security and user privacy. Being aware of the security and privacy challenges, understanding the trade-off between security and communication performance, and exploring potential countermeasures in practical systems are critical to a wide adoption of this technology in various application scenarios. This talk will discuss various security and privacy threats in emerging ISAC systems with a focus on communication-centric ISAC systems, that is, using the cellular or WiFi infrastructure for sensing. We will then examine potential mechanisms to secure ISAC systems and protect user privacy at the physical and data layers under different sensing modes. At the wireless physical (PHY) layer, an ISAC system is subject to both passive and active attacks, such as unauthorized passive sensing, unauthorized active sensing, signal spoofing, and jamming. Potential countermeasures include wireless channel/radio frequency (RF) environment obfuscation, waveform randomization, anti-jamming communication, and spectrum/RF monitoring. At the data layer, user privacy could be compromised during data collection, sharing, storage, and usage. For sensing systems powered by artificial intelligence (AI), user privacy could also be compromised during the model training and inference stages. An attacker could falsify the sensing data to achieve a malicious goal. Potential countermeasures include the application of privacy enhancing technologies (PETs), such as data anonymization, differential privacy, homomorphic encryption, trusted execution, and data synthesis. 

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4. A Privacy-Friendly Approach to Data Valuation

   Wang, Jiachen; Zhu, Yuqing; Wang, Yu-Xiang; Jia, Ruoxi; Mittal, Prateek (December 2023, Advances in neural information processing systems)

   Data valuation, a growing field that aims at quantifying the usefulness of individual data sources for training machine learning (ML) models, faces notable yet often overlooked privacy challenges. This paper studies these challenges with a focus on KNN-Shapley, one of the most practical data valuation methods nowadays. We first emphasize the inherent privacy risks of KNN-Shapley, and demonstrate the significant technical challenges in adapting KNN-Shapley to accommodate differential privacy (DP). To overcome these challenges, we introduce TKNN-Shapley, a refined variant of KNN-Shapley that is privacy-friendly, allowing for straightforward modifications to incorporate DP guarantee (DP-TKNN-Shapley). We show that DP-TKNN-Shapley has several advantages and offers a superior privacy-utility tradeoff compared to naively privatized KNN-Shapley. Moreover, even non-private TKNN-Shapley matches KNN-Shapley's performance in discerning data quality. Overall, our findings suggest that TKNN-Shapley is a promising alternative to KNN-Shapley, particularly for real-world applications involving sensitive data. 

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5. Researchers’ Experiences in Analyzing Privacy Policies: Challenges and Opportunities

   https://doi.org/10.56553/popets-2023-0111  

   Mhaidli, Abraham; Fidan, Selin; Doan, An; Herakovic, Gina; Srinath, Mukund; Matheson, Lee; Wilson, Shomir; Schaub, Florian (October 2023, Proceedings on Privacy Enhancing Technologies)

   Companies' privacy policies and their contents are being analyzed for many reasons, including to assess the readability, usability, and utility of privacy policies; to extract and analyze data practices of apps and websites; to assess compliance of companies with relevant laws and their own privacy policies, and to develop tools and machine learning models to summarize and read policies. Despite the importance and interest in studying privacy policies from researchers, regulators, and privacy activists, few best practices or approaches have emerged and infrastructure and tool support is scarce or scattered. In order to provide insight into how researchers study privacy policies and the challenges they face when doing so, we conducted 26 interviews with researchers from various disciplines who have conducted research on privacy policies. We provide insights on a range of challenges around policy selection, policy retrieval, and policy content analysis, as well as multiple overarching challenges researchers experienced across the research process. Based on our findings, we discuss opportunities to better facilitate privacy policy research, including research directions for methodologically advancing privacy policy analysis, potential structural changes around privacy policies, and avenues for fostering an interdisciplinary research community and maturing the field. 

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