Privacy-aware Kalman filtering
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 transf...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/137345 |
| _version_ | 1826113705038839808 |
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| author | Song, Yang Wang, Chong Xiao Tay, Wee Peng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Song, Yang Wang, Chong Xiao Tay, Wee Peng |
| author_sort | Song, Yang |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:27:35Z |
| format | Conference Paper |
| id | ntu-10356/137345 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:27:35Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1373452020-03-18T05:21:53Z Privacy-aware Kalman filtering Song, Yang Wang, Chong Xiao Tay, Wee Peng School of Electrical and Electronic Engineering 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Engineering::Electrical and electronic engineering Kalman Filter Inference Privacy 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. NRF (Natl Research Foundation, S’pore) Accepted version 2020-03-18T05:21:52Z 2020-03-18T05:21:52Z 2018 Conference Paper 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 9781538646588 https://hdl.handle.net/10356/137345 10.1109/ICASSP.2018.8462600 2-s2.0-85054243621 4434 4438 en © 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 application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Kalman Filter Inference Privacy Song, Yang Wang, Chong Xiao Tay, Wee Peng Privacy-aware Kalman filtering |
| title | Privacy-aware Kalman filtering |
| title_full | Privacy-aware Kalman filtering |
| title_fullStr | Privacy-aware Kalman filtering |
| title_full_unstemmed | Privacy-aware Kalman filtering |
| title_short | Privacy-aware Kalman filtering |
| title_sort | privacy aware kalman filtering |
| topic | Engineering::Electrical and electronic engineering Kalman Filter Inference Privacy |
| url | https://hdl.handle.net/10356/137345 |
| work_keys_str_mv | AT songyang privacyawarekalmanfiltering AT wangchongxiao privacyawarekalmanfiltering AT tayweepeng privacyawarekalmanfiltering |