TY - GEN
T1 - Fundamental Limits of Obfuscation for Linear Gaussian Dynamical Systems
T2 - 2021 American Control Conference, ACC 2021
AU - Fang, Song
AU - Zhu, Quanyan
N1 - Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach. Particularly, we obtain analytical formulas that capture the fundamental privacy-distortion tradeoffs when privacy masks are to be added to the outputs of the dynamical systems, while indicating explicitly how to design the privacy masks in an optimal way: The privacy masks should be colored Gaussian with power spectra shaped specifically based upon the system and noise properties.
AB - In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach. Particularly, we obtain analytical formulas that capture the fundamental privacy-distortion tradeoffs when privacy masks are to be added to the outputs of the dynamical systems, while indicating explicitly how to design the privacy masks in an optimal way: The privacy masks should be colored Gaussian with power spectra shaped specifically based upon the system and noise properties.
UR - http://www.scopus.com/inward/record.url?scp=85108737066&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108737066&partnerID=8YFLogxK
U2 - 10.23919/ACC50511.2021.9483148
DO - 10.23919/ACC50511.2021.9483148
M3 - Conference contribution
AN - SCOPUS:85108737066
T3 - Proceedings of the American Control Conference
SP - 4574
EP - 4579
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 May 2021 through 28 May 2021
ER -