Bao, E., Zhu, Y., Xiao, X., Yang, Y., Ooi, B. C., Tan, B. H. M., & Aung, K. M. M. (2022). Skellam mixture mechanism. Proceedings of the VLDB Endowment, 15(11), 2348–2360. https://doi.org/10.14778/3551793.3551798
Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train models with
), which provides rigorous privacy guarantees by injecting random noise to the gradients. This paper focuses on the scenario where sensitive data are distributed among multiple participants, who jointly train a model through
, using both
secure multiparty computation
) to ensure the confidentiality of each gradient update, and differential privacy to avoid data leakage in the resulting model. A major challenge in this setting is that common mechanisms for enforcing DP in deep learning, which inject
, are fundamentally incompatible with MPC, which exchanges
among the participants. Consequently, most existing DP mechanisms require rather high noise levels, leading to poor model utility.
Motivated by this, we propose
Skellam mixture mechanism
(SMM), a novel approach to enforcing DP on models built via federated learning. Compared to existing methods, SMM eliminates the assumption that the input gradients must be integer-valued, and, thus, reduces the amount of noise injected to preserve DP. The theoretical analysis of SMM is highly non-trivial, especially considering (i) the complicated math of DP deep learning in general and (ii) the fact that the mixture of two Skellam distributions is rather complex. Extensive experiments on various practical settings demonstrate that SMM consistently and significantly outperforms existing solutions in terms of the utility of the resulting model.
This research / project is supported by the A*STAR - MTC Programmatic
Grant Reference no. : A19E3b009
This research / project is supported by the Ministry of Education - AcRF-Tier2
Grant Reference no. : MOE2018-T2-2-091
Qatar National Research Fund Qatar Foundation