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Privacy of Information Sharing Schemes in a Cloud-based Multi-sensor Estimation Problem
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-7926-5081
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0001-9940-5929
2018 (English)In: 2018 Annual American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 998-1002, article id 8431192Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider a multi-sensor estimation problem wherein each sensor collects noisy information about its local process, which is only observed by that sensor, and a common process, which is simultaneously observed by all sensors. The objective is to assess the privacy level of (the local process of) each sensor while the common process is estimated using cloud computing technology. The privacy level of a sensor is defined as the conditional entropy of its local process given the shared information with the cloud. Two information sharing schemes are considered: a local scheme, and a global scheme. Under the local scheme, each sensor estimates the common process based on its measurement and transmits its estimate to a cloud. Under the global scheme, the cloud receives the sum of the sensors' measurements. It is shown that, in the local scheme, the privacy level of each sensor is always above a certain level which is characterized using Shannon's mutual information. It is also proved that this result becomes tight as the number of sensors increases. We also show that the global scheme is asymptotically private, i.e., the privacy loss of the global scheme decreases to zero at the rate of O(1/M) where M is the number of sensors.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 998-1002, article id 8431192
Series
Proceedings of the American Control Conference, ISSN 0743-1619
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-234705DOI: 10.23919/ACC.2018.8431192Scopus ID: 2-s2.0-85052564006ISBN: 9781538654286 (print)OAI: oai:DiVA.org:kth-234705DiVA, id: diva2:1246740
Conference
2018 Annual American Control Conference, ACC 2018, Wisconsin Center / Hilton ,Milwauke City Center Milwauke, United States, 27 June 2018 through 29 June 2018
Note

QC 20180910

Available from: 2018-09-10 Created: 2018-09-10 Last updated: 2018-09-10Bibliographically approved

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Skoglund, MikaelJohansson, Karl H.

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