Differentially private distributed convex optimization via objective perturbation

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Data Poisoning against Differentially-Private Learners: Attacks and Defenses (2019, background): “Objective perturbation injects noise into the objective function of a vanilla machine learner to train a randomized model [4, 10, 16].”
ADMM Based Privacy-Preserving Decentralized Optimization (2017, background): “However, the added noise also unavoidably compromises the accuracy of optimization results, leading to a trade-off between privacy and accuracy [19]–[21].”
Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data (2018, background): “…2011; Kifer, Smith, and Thakurta 2012; Bassily, Smith, and Thakurta 2014; Talwar, Thakurta, and Zhang 2014; Jain and Thakurta 2013; Kasiviswanathan and Jin 2016) and sample-wise distributed datasets (Huang, Mitra, and Vaidya 2015; Nozari, Tallapragada, and Cortés 2016; Han, Topcu, and Pappas 2017).”