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  • Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Title: Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Award ID(s):
2213223
PAR ID:
10569908
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Neurips
Date Published:
ISBN:
9781713899921
Format(s):
Medium: X
Location:
New Orleans, USA
Sponsoring Org:
National Science Foundation
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  4. ; (, 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. 
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  5. ; (, Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track)
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