Efficient, Secure, Differentially Private Deep Learning in the Two-Server Model

Jun Feng; Hong Sun; Pengfei Zhang; Bocheng Ren; Shunli Zhang

AAAI 2026technical0 citations

Efficient, Secure, Differentially Private Deep Learning in the Two-Server Model

Jun Feng, Hong Sun, Pengfei Zhang, Bocheng Ren, Shunli Zhang

Abstract

Existing solutions on differentially private deep learning (DPDL) either require the assumption of a trusted data server (centralized DPDL) or suffer from poor utility (local DPDL); and hence their adoptions are hampered in real-world scenarios.We present CRYPTDP, a crypto-assisted differentially private deep learning approach in the two-server model. CRYPTDP employs two non-colluding servers to collaboratively and efficiently train differentially private deep learning over the secret shares of data owners

BibTeX

@inproceedings{aaai2026_efficientsecured,
  title = {Efficient, Secure, Differentially Private Deep Learning in the Two-Server Model},
  author = {Jun Feng and Hong Sun and Pengfei Zhang and Bocheng Ren and Shunli Zhang},
  booktitle = {AAAI 2026},
  year = {2026}
}