TY - GEN
T1 - Privacy of Information Sharing Schemes in a Cloud-based Multi-sensor Estimation Problem
AU - Nekouei, Ehsan
AU - Skoglund, Mikael
AU - Johansson, Karl H.
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
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U2 - 10.23919/ACC.2018.8431192
DO - 10.23919/ACC.2018.8431192
M3 - 32_Refereed conference paper (with ISBN/ISSN)
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 998
EP - 1002
BT - 2018 Annual American Control Conference (ACC)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st Annual American Control Conference (ACC 2018)
Y2 - 27 June 2018 through 29 June 2018
ER -