Summary of Privacy-preserving Optimal Parameter Selection For Collaborative Clustering, by Maryam Ghasemian and Erman Ayday
Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
by Maryam Ghasemian, Erman Ayday
First submitted to arxiv on: 8 Jun 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 A novel approach is proposed for collaborative clustering in a semi-trusted framework, where multiple data owners combine their data while ensuring data privacy. The study investigates the impact of privacy parameters on algorithm recommendation and identifies potential risks of membership inference attacks. To mitigate these risks, differential privacy techniques are implemented to add noise and protect data confidentiality. The findings demonstrate that high-quality clustering can be achieved while maintaining data privacy, as evidenced by metrics such as the Adjusted Rand Index and Silhouette Score. This study contributes to privacy-aware data sharing, optimal algorithm and parameter selection, and effective communication between data owners and the server. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it possible for different people or organizations to share their data while keeping some information private. It looks at how to choose the right way to group similar things together (called clustering) when multiple people are sharing their data. The study finds that if you make certain choices, it can be harder to figure out which groups someone belongs to based on the shared data. To keep this from happening, the paper suggests adding some random noise to the data to protect privacy. This approach shows that you can get good results and keep data private at the same time. |
Keywords
» Artificial intelligence » Clustering » Inference
