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

Privacy of Information Sharing Schemes in a Cloud-based Multi-sensor Estimation Problem