Summary of Enhancing Scalability Of Metric Differential Privacy Via Secret Dataset Partitioning and Benders Decomposition, by Chenxi Qiu
Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition
by Chenxi Qiu
First submitted to arxiv on: 7 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper presents Metric Differential Privacy (mDP), a new approach to protecting secret data represented in general metric space. The authors extend the concept of Differential Privacy (DP) by designing a paradigm of data perturbation that can handle text data, word embeddings, geo-location data on road networks or grid maps, and other types of encoded data. To achieve this, they propose using linear programming (LP) to derive an optimal data perturbation mechanism under mDP. However, the authors note that LP may suffer from a polynomial explosion of decision variables, making it impractical for large-scale mDP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping secret information safe by adding noise to it in a special way called Metric Differential Privacy (mDP). This helps protect sensitive data like text messages or location information. The authors want to find the best way to add this noise using linear programming, but they know that this method can become too complicated when dealing with large amounts of data. |
