Differential Privacy in Linear Distributed Control Systems: Entropy Minimizing Mechanisms and Performance Tradeoffs

In distributed control systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent's da...

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Published in:	IEEE transactions on control of network systems Vol. 4; no. 1; pp. 118 - 130
Main Authors:	Yu Wang, Zhenqi Huang, Mitra, Sayan, Dullerud, Geir E.
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.03.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Communication networks Control systems Data privacy Decentralized control decision/estimation theory differential privacy Discrete time systems distributed algorithms/control Distributed control systems Distributed databases Entropy Error analysis Estimation Lower bounds Noise control Noise sensitivity Parameter sensitivity Performance enhancement Preferences Privacy Time measurement Tracking errors Tradeoffs
ISSN:	2325-5870, 2372-2533
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Abstract	In distributed control systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent's data and the performance of the control system. We formalize privacy in terms of differential privacy of agents' preference vectors. The overall control system consists of N agents with linear discrete-time coupled dynamics, each controlled to track its preference vector. Performance of the system is measured by the mean squared tracking error. We present a mechanism that achieves differential privacy by adding Laplace noise to the shared information in a way that depends on the sensitivity of the control system to the private data. We show that for stable systems the performance cost of using this type of privacy preserving mechanism grows as O(T 3 /Nε 2 ), where T is the time horizon and ε is the privacy parameter. For unstable systems, the cost grows exponentially with time. From an estimation point of view, we establish a lower-bound for the entropy of any unbiased estimator of the private data from any noise-adding mechanism that gives ε-differential privacy. We show that the mechanism achieving this lower-bound is a randomized mechanism that also uses Laplace noise.
AbstractList	In distributed control systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent's data and the performance of the control system. We formalize privacy in terms of differential privacy of agents’ preference vectors. The overall control system consists of [Formula Omitted] agents with linear discrete-time coupled dynamics, each controlled to track its preference vector. Performance of the system is measured by the mean squared tracking error. We present a mechanism that achieves differential privacy by adding Laplace noise to the shared information in a way that depends on the sensitivity of the control system to the private data. We show that for stable systems the performance cost of using this type of privacy preserving mechanism grows as [Formula Omitted], where [Formula Omitted] is the time horizon and [Formula Omitted] is the privacy parameter. For unstable systems, the cost grows exponentially with time. From an estimation point of view, we establish a lower-bound for the entropy of any unbiased estimator of the private data from any noise-adding mechanism that gives [Formula Omitted]-differential privacy. We show that the mechanism achieving this lower-bound is a randomized mechanism that also uses Laplace noise. In distributed control systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent's data and the performance of the control system. We formalize privacy in terms of differential privacy of agents' preference vectors. The overall control system consists of N agents with linear discrete-time coupled dynamics, each controlled to track its preference vector. Performance of the system is measured by the mean squared tracking error. We present a mechanism that achieves differential privacy by adding Laplace noise to the shared information in a way that depends on the sensitivity of the control system to the private data. We show that for stable systems the performance cost of using this type of privacy preserving mechanism grows as O(T 3 /Nε 2 ), where T is the time horizon and ε is the privacy parameter. For unstable systems, the cost grows exponentially with time. From an estimation point of view, we establish a lower-bound for the entropy of any unbiased estimator of the private data from any noise-adding mechanism that gives ε-differential privacy. We show that the mechanism achieving this lower-bound is a randomized mechanism that also uses Laplace noise.
Author	Yu Wang Zhenqi Huang Mitra, Sayan Dullerud, Geir E.
Author_xml	– sequence: 1 surname: Yu Wang fullname: Yu Wang email: yuwang8@illinois.edu organization: Coordinate Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA – sequence: 2 surname: Zhenqi Huang fullname: Zhenqi Huang email: zhuang25@illinois.edu organization: Coordinate Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA – sequence: 3 givenname: Sayan surname: Mitra fullname: Mitra, Sayan email: mitras@illinois.edu organization: Coordinate Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA – sequence: 4 givenname: Geir E. surname: Dullerud fullname: Dullerud, Geir E. email: dullerud@uiuc.edu organization: Coordinate Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
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Cites_doi	10.1145/2508859.2516735 10.1145/1863543.1863568 10.1007/11761679_29 10.1016/j.automatica.2013.12.001 10.1109/CDC.2014.7039718 10.1109/ACC.2015.7170902 10.1145/2566468.2566474 10.1016/j.ifacol.2015.10.331 10.1049/iet-gtd.2010.0574 10.1145/2381966.2381978 10.1109/JSTSP.2015.2425831 10.1109/CDC.2014.7039713 10.1007/11681878_14 10.1007/978-3-642-39077-7_5 10.1145/1807085.1807104 10.1145/1806689.1806786 10.1109/SP.2011.18 10.1109/FOCS.2007.66 10.1109/ACC.2016.7525222 10.1145/2684464.2684480 10.1007/978-3-540-79228-4_1 10.1109/CDC.2013.6760417 10.1016/0041-1647(78)90027-8 10.1109/CDC.2014.7039717 10.1109/Allerton.2012.6483414 10.1109/CDC.2014.7039715 10.1145/1806689.1806787 10.1109/CDC.2014.7040430 10.1109/TAC.2013.2283096 10.1109/TNET.2012.2208983 10.1109/MPE.2009.934876
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References	ref35 ref13 ref34 ref12 chatzikokolakis (ref19) 2013 ref37 ref36 ref14 ref31 ref30 ref33 ref32 ref10 ref2 ref1 ref17 dwork (ref24) 2006; 4004 ref18 shi (ref3) 2011; 2 wang (ref4) 2016 ref23 ref26 ref25 ref20 (ref15) 2016 ref22 ref21 nozari (ref28) 2015; 48 ref27 ref29 ref8 ref7 ref9 ref6 ref5 dwork (ref16) 2006 dwork (ref11) 2006
References_xml	– ident: ref5 doi: 10.1145/2508859.2516735 – ident: ref21 doi: 10.1145/1863543.1863568 – volume: 4004 start-page: 486 year: 2006 ident: ref24 article-title: Our data, ourselves: Privacy via distributed noise generation publication-title: Proc Adv Cryptol -EUROCRYPT 2006 (Series Lecture Notes in Computer Science) doi: 10.1007/11761679_29 – ident: ref2 doi: 10.1016/j.automatica.2013.12.001 – ident: ref1 doi: 10.1145/2508859.2516735 – ident: ref31 doi: 10.1109/CDC.2014.7039718 – ident: ref30 doi: 10.1109/ACC.2015.7170902 – ident: ref35 doi: 10.1145/2566468.2566474 – volume: 48 start-page: 203 year: 2015 ident: ref28 article-title: Differentially private average consensus with optimal noise selection publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2015.10.331 – ident: ref10 doi: 10.1049/iet-gtd.2010.0574 – ident: ref17 doi: 10.1145/2381966.2381978 – ident: ref34 doi: 10.1109/JSTSP.2015.2425831 – ident: ref36 doi: 10.1109/CDC.2014.7039713 – start-page: 265 year: 2006 ident: ref16 article-title: Calibrating noise to sensitivity in private data analysis publication-title: Proc Conf Theory of Cryptography doi: 10.1007/11681878_14 – start-page: 82 year: 2013 ident: ref19 article-title: Broadening the scope of differential privacy using metrics publication-title: Privacy Enhancing Technologies doi: 10.1007/978-3-642-39077-7_5 – start-page: 179 year: 2016 ident: ref4 article-title: Defending against sybil devices in crowdsourced mapping services publication-title: Proc Annu Int Conf Mobile Syst Appl Serv – ident: ref23 doi: 10.1145/1807085.1807104 – ident: ref14 doi: 10.1145/1806689.1806786 – ident: ref7 doi: 10.1109/SP.2011.18 – ident: ref22 doi: 10.1109/FOCS.2007.66 – ident: ref27 doi: 10.1109/ACC.2016.7525222 – start-page: 1 year: 2006 ident: ref11 article-title: Differential privacy publication-title: Automata Languages and Programming – ident: ref32 doi: 10.1145/2684464.2684480 – year: 2016 ident: ref15 article-title: What's new in iOS 10.0 – ident: ref12 doi: 10.1007/978-3-540-79228-4_1 – ident: ref18 doi: 10.1109/CDC.2013.6760417 – ident: ref37 doi: 10.1016/0041-1647(78)90027-8 – ident: ref26 doi: 10.1109/CDC.2014.7039717 – ident: ref25 doi: 10.1109/Allerton.2012.6483414 – ident: ref9 doi: 10.1109/CDC.2014.7039715 – ident: ref13 doi: 10.1145/1806689.1806787 – volume: 2 year: 2011 ident: ref3 article-title: Privacy-preserving aggregation of time-series data publication-title: Proc 19th Annu Netw Distrib Syst Security Symp – ident: ref20 doi: 10.1145/1806689.1806786 – ident: ref29 doi: 10.1109/CDC.2014.7040430 – ident: ref33 doi: 10.1109/TAC.2013.2283096 – ident: ref6 doi: 10.1109/TNET.2012.2208983 – ident: ref8 doi: 10.1109/MPE.2009.934876
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SubjectTerms	Communication networks Control systems Data privacy Decentralized control decision/estimation theory differential privacy Discrete time systems distributed algorithms/control Distributed control systems Distributed databases Entropy Error analysis Estimation Lower bounds Noise control Noise sensitivity Parameter sensitivity Performance enhancement Preferences Privacy Time measurement Tracking errors Tradeoffs
Title	Differential Privacy in Linear Distributed Control Systems: Entropy Minimizing Mechanisms and Performance Tradeoffs
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