Abstract
A privacy-preserving average consensus algorithm is proposed that synergizes the Beaver triple in secret sharing theory and noise obfuscation. The algorithm safeguards the initial values of agents against passive adversaries in a multiagent system. It is proved that the proposed algorithm can concurrently ensure average consensus and privacy, while also reducing the online computation and communication overhead compared to encryption-based ones. In addition, it imposes a less stringent condition for privacy preservation compared to certain noise-obfuscation techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 4801-4808 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 70 |
| Issue number | 7 |
| DOIs | |
| State | Published - 2025 |
Funding
Received 30 September 2024; revised 27 January 2025; accepted 1 February 2025. Date of publication 6 February 2025; date of current version 1 July 2025. The work of Peng Wang, Lulu Pan, Haibin Shao, and Ning Li are supported in part by the National Natural Science Foundation of China under Grant 62203302, Grant 62188101, Grant 62373244, Grant 62103278, and Grant 62273230, and in part by the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under Grant SL2023MS011. Recommended by Associate Editor Y. Wang. (Corresponding author: Yang Lu.) Peng Wang, Lulu Pan, Haibin Shao, and Ning Li are with the Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).
Keywords
- Beaver triple
- consensus
- multiagent system
- privacy