| Title | PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Wang, Chong Xiao, Song, Yang, Tay, Wee Peng |
| Conference Name | 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
| Keywords | Cramér-Rao lower bound, Cramér-Rao lower bounds, data privacy, decentralized sensors., Indexes, inference allowance, Information Privacy, linear model, Loss measurement, Metrics, Noise measurement, parameter estimation, parameter privacy preservation, perturbation, Perturbation methods, predefined privacy gain threshold, privacy, privacy gain functions, privacy models and measurement, private parameter estimation, pubcrawl, public parameter estimation, sensor fusion, Sensor networks, Silicon, utility loss, Wireless sensor networks |
| 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. |
| DOI | 10.1109/GlobalSIP.2018.8646390 |
| Citation Key | wang_preserving_2018 |
PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS