Summary of Differential Privacy Enables Fair and Accurate Ai-based Analysis Of Speech Disorders While Protecting Patient Data, by Soroosh Tayebi Arasteh et al.
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data
by Soroosh Tayebi Arasteh, Mahshad Lotfinia, Paula Andrea Perez-Toro, Tomas Arias-Vergara, Mahtab Ranji, Juan Rafael Orozco-Arroyave, Maria Schuster, Andreas Maier, Seung Hee Yang
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 explores the application of differential privacy (DP) to pathological speech analysis, focusing on the trade-offs between privacy, diagnostic accuracy, and fairness. The authors investigate the impact of DP on diagnosing speech disorders using a large dataset of recordings from 2,839 German-speaking participants. They demonstrate that training with high levels of DP results in a maximum accuracy reduction of 3.85%. To highlight real-world privacy risks, the authors show the vulnerability of non-private models to explicit gradient inversion attacks and the effectiveness of DP in mitigating these risks. The paper also validates its approach on a dataset of Spanish-speaking Parkinson’s disease patients and performs a comprehensive fairness analysis. Overall, the study establishes that DP can balance privacy and utility in speech disorder detection while highlighting unique challenges in privacy-fairness trade-offs for speech data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to keep people’s speech private when trying to diagnose speech disorders. It uses a special technique called differential privacy (DP) to make sure that the models don’t learn too much about individual people. The researchers tested DP on a big dataset of German-speaking recordings and found that it reduced accuracy by just 3.85%. They also showed that non-private models can be tricked into revealing private information, but DP helps prevent this. To see if their approach would work in other languages and situations, they tested it on Spanish-speaking Parkinson’s disease patients. The study found that DP can balance privacy and usefulness in diagnosing speech disorders, but there are still challenges to overcome. |
