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Large-scale multi-agent cooperative control problems have materially enjoyed the scalability, adaptivity, and flexibility of decentralized optimization. However, due to the mandatory iterative communications between the agents and the system operator, the decentralized architecture is vulnerable to malicious attacks and privacy breach. Current research on addressing privacy preservation of both agents and the system operator in cooperative decentralized optimization with strongly coupled objective functions and constraints is still primitive. To fill in the gaps, this paper proposes a novel privacy-preserving decentralized optimization paradigm based on Paillier cryptosystem. The proposed paradigm achieves ideal correctness and security, as well as resists attacks from a range of adversaries. The efficacy and efficiency of the proposed approach are verified via numerical simulations and a real-world physical platform.
Privacy concerns with sensitive data are receiving increasing attention. In this paper, we study local differential privacy (LDP) in interactive decentralized optimization. By constructing random local aggregators, we propose a framework to amplify L
This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerat
Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density func
Distributed Virtual Private Networks (dVPNs) are new VPN solutions aiming to solve the trust-privacy concern of a VPNs central authority by leveraging a distributed architecture. In this paper, we first review the existing dVPN ecosystem and debate o
Federated learning enables a large number of clients to participate in learning a shared model while maintaining the training data stored in each client, which protects data privacy and security. Till now, federated learning frameworks are built in a