Robust Privacy Preserving Linear Regression Using Beta-Divergence

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  • プライバシ保護機能をもつベータダイバージェンスを用いた ロバスト線形回帰

Abstract

This paper proposes a robust privacy preserving linear regression using beta-divergence. The proposed method encrypts sensitive data using an additive homomorphic encryption to achieve privacy preservation. As in the additive homomorphic encryption, the value of the data to be encrypted must be an integer. Although a weight function in a robust linear regression removes outliers, the output value of the weight function is a real number. The proposed method approximates the weight function as a polynomial, such that the output value of the approximated weight function is an integer. Numerical simulations demonstrate the effectiveness of the proposed method.

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