More Like this

1. Disaster privacy/privacy disaster

   https://doi.org/10.1002/asi.24353  

   Sanfilippo, Madelyn R.; Shvartzshnaider, Yan; Reyes, Irwin; Nissenbaum, Helen; Egelman, Serge (March 2020, Journal of the Association for Information Science and Technology)
2. Transparent Privacy is Principled Privacy

   https://doi.org/10.1162/99608f92.b5d3faaa  

   Gong, Ruobin (June 2022, Harvard Data Science Review)
3. When Differential Privacy Implies Syntactic Privacy

   https://doi.org/10.1109/TIFS.2022.3177953  

   Ekenstedt, Emelie; Ong, Lawrence; Liu, Yucheng; Johnson, Sarah; Yeoh, Phee Lep; Kliewer, Joerg (January 2022, IEEE Transactions on Information Forensics and Security)
4. Do Privacy Labels Answer Users’ Privacy Questions?

   Zhang, S.; Sadeh, N. (February 2023, Workshop on Usable Security and Privacy)

   Inspired by earlier academic research, iOS app privacy labels and the recent Google Play data safety labels have been introduced as a way to systematically present users with concise summaries of an app’s data practices. Yet, little research has been conducted to determine how well today’s mobile app privacy labels address people’s actual privacy concerns or questions. We analyze a crowd-sourced corpus of privacy questions collected from mobile app users to determine to what extent these mobile app labels actually address users’ privacy concerns and questions. While there are differences between iOS labels and Google Play labels, our results indicate that an important percentage of people’s privacy questions are not answered or only partially addressed in today’s labels. Findings from this work not only shed light on the additional fields that would need to be included in mobile app privacy labels but can also help inform refinements to existing labels to better address users’ typical privacy questions. 

   more » « less
5. Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy

   Kazan, Z; Reiter, J P (December 2024, Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track)