3 papers
cs.GT2026
Privacy as Commodity: MFG-RegretNet for Large-Scale Privacy Trading in Federated Learning
Kangkang Sun, Jianhua Li, Xiuzhen Chen +2
Federated Learning (FL) has emerged as a prominent paradigm for privacy-preserving distributed machine learning, yet two fundamental challenges hinder its large-scale adoption. Fir…
cs.CR2025
Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption
Minjie Wang, Jinguang Han, Weizhi Meng
In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage problem still threatens the pr…
cs.CR2023
Blockchain-based Privacy-Preserving Public Key Searchable Encryption with Strong Traceability
Yue Han, Jinguang Han, Weizhi Meng +2
Public key searchable encryption (PKSE) scheme allows data users to search over encrypted data. To identify illegal users, many traceable PKSE schemes have been proposed. However,…