Summary of On the Complexity Of Differentially Private Best-arm Identification with Fixed Confidence, by Achraf Azize et al.
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
by Achraf Azize, Marc Jourdan, Aymen Al Marjani, Debabrota Basu
First submitted to arxiv on: 5 Sep 2023
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 In a machine learning setting where data privacy is crucial, researchers investigate Best Arm Identification (BAI) problems that ensure fixed confidence under -global Differential Privacy (DP). The study quantifies the cost of privacy by deriving a lower bound on the sample complexity for any BAI algorithm satisfying -global DP. The findings suggest two privacy regimes: high-privacy, where the hardness depends on privacy and Total Variation Characteristic Time; and low-privacy, where the classical non-private lower bound applies. A new algorithm, AdaP-TT, is proposed as an -global DP variant of Top Two, which achieves a good privacy-utility trade-off through adaptive episodes and Laplace noise. Theoretical results are validated through experimental analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Best Arm Identification (BAI) problems help design clinical trials, tune hyperparameters, or conduct user studies while keeping data private. Researchers study BAI with fixed confidence under -global Differential Privacy (DP). They find that too much privacy costs more samples to collect. A new algorithm called AdaP-TT helps balance privacy and usefulness by adding noise to the results. |
Keywords
* Artificial intelligence * Machine learning
