Abstract
In this paper, we focus on the privacy protection of sensitive data and develop a distributed logistic regression that satisfies differential privacy. Distributed differential privacy is achieved by perturbing the distributed algorithm output. Further, to prevent privacy leakage occurring during the computer interaction process, we propose a distributed logistic variable perturbation algorithm based on an alternating direction method of multipliers (ADMM) algorithm. Further, the theoretical bounds of the algorithms are provided. Experiments show that the proposed algorithms can effectively analyze distributed storage data and protect their privacy.
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