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|Abstract:||Copyright 2018 by the author(s). This paper proposes a distributed privacy-preserving sparse PCA (DPS-PCA) algorithm that generates a minimax-optimal sparse PCA estimator under differential privacy constraints. In a distributed optimization framework, data providers can use this algorithm to collaboratively analyze the union of their data sets while limiting the disclosure of their private information. DPS-PCA can recover the leading eigenspace of the population covariance at a geometric convergence rate, and simultaneously achieves the optimal minimax statistical error for high-dimensional data. Our algorithm provides fine-tuned control over the tradeoff between estimation accuracy and privacy preservation. Numerical simulations demonstrate that DPS-PCA significantly outperforms other privacy-preserving PCA methods in terms of estimation accuracy and computational efficiency.|
|Citation:||Ge, J, Wang, Z, Wang, M, Liu, H. (2018). Minimax-optimal privacy-preserving sparse PCA in distributed systems. International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 1589 - 1598|
|Pages:||1589 - 1598|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||International Conference on Artificial Intelligence and Statistics, AISTATS 2018|
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