Abstract
In this paper, we propose a privacy-preserving event-triggered quantized average consensus algorithm that allows agents to calculate the average of their initial values without revealing to other agents their specific value. We assume that agents (nodes) interact with other agents via directed communication links (edges), forming a directed communication topology (digraph). The proposed distributed algorithm can be followed by any agent wishing to maintain its privacy (i.e., not reveal the initial value it contributes to the average) to other, possibly multiple, curious but not malicious agents. Curious agents try to identify the initial values of other agents, but do not interfere in the computation in any other way. We develop a distributed strategy that allows agents while processing and transmitting quantized information, to preserve the privacy of their initial quantized values and at the same time to obtain, after a finite number of steps, the exact average of the initial values of the nodes. Illustrative examples demonstrate the validity and performance of our proposed algorithm.
Original language | English |
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Title of host publication | Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020 |
Publisher | IEEE |
Pages | 6246-6253 |
Number of pages | 8 |
ISBN (Electronic) | 9781728174471 |
DOIs | |
Publication status | Published - 14 Dec 2020 |
MoE publication type | A4 Conference publication |
Event | IEEE Conference on Decision and Control - Virtual, Online, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 Conference number: 59 |
Publication series
Name | Proceedings of the IEEE Conference on Decision & Control |
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Publisher | IEEE |
ISSN (Print) | 0743-1546 |
Conference
Conference | IEEE Conference on Decision and Control |
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Abbreviated title | CDC |
Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 14/12/2020 → 18/12/2020 |
Keywords
- Average consensus
- Event-triggered
- Privacy preservation
- Quantized averaging