Journal
2020 AMERICAN CONTROL CONFERENCE (ACC)
Volume -, Issue -, Pages 1465-1470Publisher
IEEE
DOI: 10.23919/acc45564.2020.9147726
Keywords
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Funding
- NSERC [RGPIN-5287-2018, RGPAS-2018-522686]
- Pierre Arbour Foundation
- FRQNT
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Real-time data processing for emerging systems such as intelligent transportation systems requires estimating variables based on privacy-sensitive data gathered from individuals, e.g., their location traces. In this paper, we present a privacy-preserving interval observer architecture for a multi-agent system, where a bounded privacy-preserving noise is added to each participant's data and is subsequently taken into account by the observer. The estimates published by the observer guarantee differential privacy for the agents' data, which means that their statistical distribution is not too sensitive to certain variations in any single agent's signal. A numerical simulation illustrates the behavior of the proposed architecture.
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