Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation
Author:
Affiliation:
1. School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
2. School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
Funder
National Natural Science Foundation of China
Shenzhen Science and Technology Program
Shenzhen Research Institute of Big Data
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Control and Optimization,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/7782633/9828546/09999278.pdf?arnumber=9999278
Reference21 articles.
1. Differentially Private Distributed Convex Optimization via Functional Perturbation
2. Differentially Private Distributed Optimization
3. Enabling Privacy-Preservation in Decentralized Optimization
4. Privacy preserving distributed optimization using homomorphic encryption
5. Gradient-based learning applied to document recognition
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1. Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
2. Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus;IEEE Transactions on Information Forensics and Security;2024
3. Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation;IEEE Control Systems Letters;2023
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