Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors

Authors: 

Timothy Stevens, Christian Skalka, and Christelle Vincent, University of Vermont; John Ring, MassMutual; Samuel Clark, Raytheon; Joseph Near, University of Vermont

Abstract: 

Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring their own challenges. Many techniques require a trusted third party or else add too much noise to produce useful models. Recent advances in secure aggregation using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale. We present a new federated learning protocol that leverages a novel differentially private, malicious secure aggregation protocol based on techniques from Learning With Errors. Our protocol outperforms current state-of-the art techniques, and empirical results show that it scales to a large number of parties, with optimal accuracy for any differentially private federated learning scheme.

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BibTeX
@inproceedings {280010,
title = {Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
address = {Boston, MA},
url = {https://www.usenix.org/conference/usenixsecurity22/presentation/stevens},
publisher = {USENIX Association},
month = aug,
}