Designing Differentially Private Estimators in High Dimensions

• URL: http://arxiv.org/abs/2006.01944v3
• Date: Sat, 18 Jul 2020 17:01:13 GMT
• Title: Designing Differentially Private Estimators in High Dimensions
• Authors: Aditya Dhar, Jason Huang
• Abstract summary: We study differentially private mean estimation in a high-dimensional setting. Recent work in high-dimensional robust statistics has identified computationally tractable mean estimation algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study differentially private mean estimation in a high-dimensional setting. Existing differential privacy techniques applied to large dimensions lead to computationally intractable problems or estimators with excessive privacy loss. Recent work in high-dimensional robust statistics has identified computationally tractable mean estimation algorithms with asymptotic dimension-independent error guarantees. We incorporate these results to develop a strict bound on the global sensitivity of the robust mean estimator. This yields a computationally tractable algorithm for differentially private mean estimation in high dimensions with dimension-independent privacy loss. Finally, we show on synthetic data that our algorithm significantly outperforms classic differential privacy methods, overcoming barriers to high-dimensional differential privacy.

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