Abstract
A growing number of large-scale monitoring and control systems enabling a more intelligent infrastructure, such as smart grids and intelligent transportation systems, rely for their operation on private data obtained from individuals or from privacy-sensitive entities more generally. This entry discusses how to capture and enforce formal quantitative privacy constraints to design estimation and control algorithms that can trade off performance for the level of privacy offered. A state-of-the art definition of privacy called differential privacy, which provides guarantees against adversaries with arbitrary side information, is presented. It is shown that tools from systems and control can be used to enforce differential privacy guarantees when publishing real-time statistics or solving distributed optimization and control problems based on sensitive data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Bibliography
Cortés J, Dullerud GE, Han S, Le Ny J, Mitra S, Pappas GJ (2016) Differential privacy in control and network systems. In: IEEE conference on decision and control (CDC)
Degue KH, Le Ny J (2017) On differentially private Kalman filtering. In: IEEE global conference on signal and information processing (GlobalSIP)
Duncan G, Lambert D (1986) Disclosure-limited data dissemination. J Am Stat Assoc 81(393):10–28
Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 9(3–4):211–407
Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Proceedings of the third theory of cryptography conference, New York
Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891
Hsu J, Huang Z, Roth A, Wu Z (2016) Jointly private convex programming. In: Proceedings of the 27th annual ACM-SIAM symposium on discrete algorithms (SODA)
Huang Z, Mitra S, Dullerud G (2012) Differentially private iterative synchronous consensus. In: Proceedings of the 2012 ACM workshop on privacy in the electronic society
Le Ny J (2013) On differentially private filtering for event streams. In: IEEE conference on decision and control
Le Ny J (2020) Differential privacy for dynamic data. Springer
Le Ny J, Pappas GJ (2014) Differentially private filtering. IEEE Trans Autom Control 59(2):341–354
Manitara NE, Hadjicostis CN (2013) Privacy-preserving asymptotic average consensus. In: Proceedings of the European control conference, Zurich
Mo Y, Murray R (2016) Privacy preserving average consensus. IEEE Trans Autom Control 62(2):753–765
Nozari E, Tallapragada P, Cortés J (2017) Differentially private average consensus: obstructions, trade-offs, and optimal algorithm design. Automatica 81:221–231
Sankar L, Rajagopalan SR, Poor HV (2013) Utility-privacy tradeoffs in databases: an information-theoretic approach. IEEE Trans Inf Forensics Secur 8(6):838–852
Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl-Based Syst 10(05):557–570
Trappe W, Sankar L, Poovendran R, Lee H, Capkun S (2015) Special issue on signal and information processing for privacy. IEEE J Sel Top Signal Process 9(7):1173–1175
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2021 Springer-Verlag London Ltd., part of Springer Nature
About this entry
Cite this entry
Le Ny, J. (2021). Privacy in Network Systems. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100139-1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5102-9_100139-1
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5102-9
Online ISBN: 978-1-4471-5102-9
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering