Secure Multi-party Differential Privacy

By Peter Kairouz, Sewoong Oh, Pramod Viswanath. 2015

52 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The Composition Theorem for Differential Privacy (2013, background): “This paper was presented in part at the International Conference on Machine Learning in 2015 [KOV15].”
PacGAN: The Power of Two Samples in Generative Adversarial Networks (2017, methodology): “This was used to prove strong composition theorems in differential privacy [34] and to identify the optimal differentially-private mechanisms under local privacy [32] and multi-party communications [33].”
Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties (2020, background): “In the strong model of local differential privacy (LDP) [53, 30, 51, 52, 49], each user randomizes its input locally before sending it to an untrusted aggregator.”
Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design (2015, background): “…also deals with scenarios where no trust exists among agents, the maximum number of agents that can collude (without the privacy of others being breached) is bounded, whereas using differential privacy provides immunity against arbitrary collusions [Kairouz et al., 2015, Pettai and Laud, 2015].”
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible (2017, background): “Existing works primarily focus on basic estimation problems such as means and discrete densities [8, 7, 1], or some function calculations [21].”
Differentially private multi-party computation (2014, background): “This paper is a follow up to our work in [1], where we studied multi-party computation under (", 0) differential privacy.”