Summary of Differential Privacy Of Cross-attention with Provable Guarantee, by Yingyu Liang et al.
Differential Privacy of Cross-Attention with Provable Guarantee
by Yingyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel differential privacy (DP) data structure to ensure the privacy security of cross-attention mechanisms used in various AI applications. Cross-attention, a fundamental module in many AI systems, may contain sensitive information about model providers and users. The proposed data structure has a theoretical guarantee and provides (,)-DP with additive error and relative error bounds. It also demonstrates robustness to adaptive queries that aim to attack the cross-attention system. This work is promising for developing privacy algorithms in large generative models (LGMs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to keep AI systems private by protecting sensitive information about model providers and users. Cross-attention is an important part of many AI applications, but it can contain private data. The authors design a special structure that makes sure cross-attention stays private while still providing accurate results. This work shows that the privacy algorithm works even when users try to attack the system intentionally. |
Keywords
» Artificial intelligence » Cross attention
