Summary of An Efficient Difference-of-convex Solver For Privacy Funnel, by Teng-hui Huang and Hesham El Gamal
An Efficient Difference-of-Convex Solver for Privacy Funnel
by Teng-Hui Huang, Hesham El Gamal
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Information Theory (cs.IT)
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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 proposed solver efficiently solves the privacy funnel (PF) method by leveraging its difference-of-convex (DC) structure. The closed-form update equation allows straightforward application to both known and unknown distribution settings. For known distributions, convergence is proven, outperforming state-of-the-art approaches in characterizing the privacy-utility trade-off. The insights apply to unknown distributions with labeled empirical samples available. An alternating minimization solver satisfies the fundamental Markov relation of PF, unlike previous variational inference-based solvers. Evaluation with MNIST and Fashion-MNIST datasets shows that under comparable reconstruction quality, an adversary suffers from higher prediction error due to clustering compressed codes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve a problem called privacy funnel (PF). It makes it easier to balance privacy and utility in a situation where you don’t know the exact distribution of data. The method uses a clever mathematical trick that allows it to work well in both known and unknown situations. The results show that this approach is better than others at solving this problem, and it also protects the private information more effectively. |
Keywords
* Artificial intelligence * Clustering * Inference
