Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/137345
Title: | Privacy-aware Kalman filtering | Authors: | Song, Yang Wang, Chong Xiao Tay, Wee Peng |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2018 | Source: | Song, Y., Wang, C. X., & Tay, W. P. (2018). Privacy-aware Kalman filtering. Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4434-4438. doi:10.1109/icassp.2018.8462600 | Conference: | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | Abstract: | We are concerned with a privacy-preserving problem in Kalman filter: a sensor releases a set of measurements to fusion center, who has perfect knowledge of the dynamical model, to allow it to estimate the public state, while prevent it from estimating the private state. We propose to linearly transform the original observation into a lower dimensional space before sending them to fusion center. Two privacy-utility tradeoffs are formulated: one concerns only at the current time step and the other concerns over two time steps. The transformation that leads to the optimal tradeoff can be found in closed-form. The privacy (estimation of private state) and utility (estimation of public state) are measured based on recursive Bayesian Cramér-Rao bound. | URI: | https://hdl.handle.net/10356/137345 | ISBN: | 9781538646588 | DOI: | 10.1109/ICASSP.2018.8462600 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICASSP.2018.8462600 | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Conference Papers |
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Privacy-Aware Kalman Filtering.pdf | 277.86 kB | Adobe PDF | View/Open |
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