TPRC 2024: The 52nd Research Conference on Communications, Information and Inter

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Abstract:
Differential privacy (DP) is a state-of-the-art privacy-preserving mechanism. In this paper, we argue that interpreting users’ personalized and contextual privacy choices is key to applying DP in actual use cases. Through the lens of contextual integrity (CI), we conducted a proof-ofconcept survey (N=23) to examine how app types and information receivers affected people’s perceived appropriateness of information disclosures and their choices of the privacy-data utility tradeoff in DP. Through the exploratory analysis, we revealed people’s diverse privacy choices, which were affected by contexts. Further, people’s perceived appropriateness of information flows and desired tradeoff between privacy and data utility in DP were consistent. Based on the findings, we point out the technical questions and uncertainty about DP and stress the importance of understanding users’ personalized and contextual privacy choices to avoid misalignment between app and users and, therefore, enhance the usability of DP. This research sheds light on making DP more socially aware and adaptive to user needs via integration with the CI framework.

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Abstract:
Since the reversal of Roe v. Wade in the Summer of 2022 in the United States, rising concerns have emerged towards privacy, data protection, and digital trust concerning reproductive health data. Such health data has become both a commodity in the commercial world and source of discovery in legal proceedings that attempt to incriminate women for seeking abortions. Digital footprints created via location data, private conversations on platforms, and internet search history lead to a constant state of surveillance that threatens privacy and freedom of movement for women throughout the United States. We conducted an online survey via Amazon Mechanical Turk in order to identify the largest user concerns towards privacy of health tracking and period tracking applications. A large number of users were concerned with the privacy of their reproductive information and were found to have deleted period tracking applications due to these concerns. Additionally, we sought to identify what actions would lead users to feel more comfortable using these applications, identifying the largest being the localized storage of this data on their device. These findings suggest that users may be more likely to use these applications if their privacy concerns are addressed.

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Abstract:
Differential Privacy (DP) is a powerful technology, but not well-suited to protecting corporate proprietary information while computing aggregate industry-wide statistics. We elucidate this scenario with an example of cybersecurity management data, and consider an alternative approach that relies on a pragmatic assessment of harm to add noise to the data.