Combining Differential Privacy and Secure Multiparty Computation

98 citations recorded by the source corpus.

How researchers see this paper

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A Hybrid Approach to Privacy-Preserving Federated Learning (2018, background): “In [33] the authors’ protocols are tailored to inner join tables and counting the number of values in an array.”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, background): “A few works have explored combining MPC with differential privacy to provide end-too-end privacy for computations with distributed data (Pettai and Laud, 2015; Kairouz et al., 2015; He et al., 2017), but this area is in its infancy and many challenging problems need to be solved before the impacts o”
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019, methodology): “Inspired from the hybrid methodology [32], a recent paper [38] also proposed a hybrid solution that provides strong privacy guarantees while still enabling good model performance.”
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].”