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- W2920090984 abstract "We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramer-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable." @default.
- W2920090984 created "2019-03-11" @default.
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- W2920090984 date "2018-11-01" @default.
- W2920090984 modified "2023-10-16" @default.
- W2920090984 title "PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS" @default.
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- W2920090984 doi "https://doi.org/10.1109/globalsip.2018.8646390" @default.
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