Summary of Optimizing Privacy and Utility Tradeoffs For Group Interests Through Harmonization, by Bishwas Mandal et al.
Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization
by Bishwas Mandal, George Amariucai, Shuangqing Wei
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our paper proposes a novel problem formulation for addressing the privacy-utility tradeoff in scenarios involving two distinct user groups with unique sets of private and utility attributes. Unlike previous studies, we introduce a collaborative data-sharing mechanism between these groups through a trusted third party, leveraging adversarial privacy techniques to internally sanitize data and eliminate the need for manual annotation or auxiliary datasets. This approach ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features. Moreover, even with auxiliary datasets containing raw data, analysts or adversaries are unable to accurately deduce private features. We demonstrate the effectiveness of our approach using synthetic and real-world datasets, showcasing its ability to balance the conflicting goals of privacy and utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have two groups of people, each with their own secrets (private attributes) and things they want to know (utility attributes). We came up with a new way for these groups to share information without putting anyone’s secrets at risk. This is different from what other researchers have done before, which often relied on extra data or manual work. Our method uses special techniques to make sure the private information stays safe while still allowing people to learn useful things. Even if someone had extra information about the raw data, they wouldn’t be able to figure out the secrets. We tested our approach using fake and real-world data and showed that it can find a good balance between keeping secrets safe and giving people the information they need. |
