Abstract
Hypothesis testing is the fundamental theory behind decision-making and therefore plays a critical role in information systems. A prominent example is machine learning, which is currently developed and applied to a wide range of applications. However, besides the utilities, hypothesis testing can also be implemented for an illegitimate purpose to infer on people’s privacy. Thus, the development of hypothesis testing techniques further increases the privacy leakage risks. Accordingly, the research on privacy-by-design techniques that enhance the privacy against adversarial hypothesis testing receives more and more attention recently. In this chapter, the problem of privacy against adversarial hypothesis testing is formulated in the presence of a distortion source. Information-theoretic fundamental bounds on the optimal privacy performance and corresponding privacy-enhancing technologies are first discussed under the assumption of independent and identically distributed adversarial observations. The discussion is then extended to considering a privacy problem model with memory. In the end, applications of the theoretic results and privacy-enhancing technologies to the smart meter privacy problem are illustrated.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
This setting assumes an energy measure precision.
References
Van Trees HL (2001) Detection, estimation, and modulation theory, Part I. Wiley
Varshney PK (1996) Distributed detection and data fusion. Springer, New York
Chernoff H (1952) A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Ann Math Stat 23:493–507
Cover TM, Thomas JA (2006) Elements of information theory. Wiley
Mhanna M, Piantanida P (2015) On secure distributed hypothesis testing. In: Proceeding of IEEE ISIT 2015, pp 1605–1609
Barni M, Tondi B (2016) Source distinguishability under distortion-limited attack: an optimal transport perspective. IEEE Trans Inf Forensics Secur 11:2145–2159
Sreekumar S, Gündüz D, Cohen A (2018) Distributed hypothesis testing under privacy constraints. Proc IEEE ITW 2018:470–474
Liao J, Sankar L, Tan VYF, Calmon FP (2018) Hypothesis testing under mutual information privacy constraints in the high privacy regime. IEEE Trans Inf Forensics Secur 13:1058–1071
Calmon FP, Fawaz N (2012) Privacy against statistical inference. Proc Allerton 2012:1401–1408
Li Z, Oechtering TJ (2015) Privacy-aware distributed Bayesian detection. IEEE J Sel Top Signal Process 9:1345–1357
Li Z, Oechtering TJ, Skoglund M (2016) Privacy-preserving energy flow control in smart grids. In: Proceedings of IEEE ICASSP 2016, pp 2194–2198
Nadendla VSS, Varshney PK (2016) Design of binary quantizers for distributed detection under secrecy constraints. IEEE Trans Signal Process 64:2636–2648
Li Z, Oechtering TJ (2017) Privacy-constrained parallel distributed Neyman–Pearson test. IEEE Trans Signal Inf Process Netw 3:77–90
You Y, Li Z, Oechtering TJ (2018) Optimal privacy-enhancing and cost-efficient energy management strategies for smart grid consumers. Proc IEEE SSP 2018:826–830
Li Z, Oechtering TJ (2018) Privacy-utility management of hypothesis tests. In: Proceedings of IEEE ITW 2018, pp 1–5
Li Z, Oechtering TJ, Gündüz D (2019) Privacy against a hypothesis testing adversary. IEEE Trans Inf Forensics Secur 14:1567–1581
Giaconi G, Gündüz D, Poor HV (2018) Privacy-aware smart metering: progress and challenges. IEEE Signal Process Mag 35:59–78
Rajagopalan SR, Sankar L, Mohajer S, Poor HV (2011) Smart meter privacy: a utility-privacy framework. In: Proceedings of IEEE SmartGridComm 2011, pp 190–195
Garcia FD, Jacobs B (2010) Privacy-friendly energy-metering via homomorphic encryption. In: Proceedings of STM 2010, pp 226–238
Efthymiou C, Kalogridis G (2010) Smart grid privacy via anonymization of smart metering data. In: Proceedings of IEEE SmartGridComm 2010, pp 238–243
Gündüz D, Gómez-Vilardebó J (2013) Smart meter privacy in the presence of an alternative energy source. In: Proceedings of IEEE ICC 2013, pp 2027–2031
Gómez-Vilardebó J, Gündüz D (2015) Smart meter privacy for multiple users in the presence of an alternative energy source. IEEE Trans Inf Forensics Secur 10:132–141
Giaconi G, Gündüz D, Poor HV (2018) Smart meter privacy with renewable energy and an energy storage device. IEEE Trans Inf Forensics Secur 13:129–142
Yao J, Venkitasubramaniam P (2013) On the privacy-cost tradeoff of an in-home power storage mechanism. In: Proceedings of Allerton 2013, pp 115–122
Tan O, Gómez-Vilardebó J, Gündüz D (2017) Privacy-cost trade-offs in demand-side management with storage. IEEE Trans Inf Forensics Secur 12:1458–1469
Li S, Khisti A, Mahajan A (2018) Information-theoretic privacy for smart metering systems with a rechargeable battery. IEEE Trans Inf Theory 64:3679–3695
Ács G, Castelluccia C (2011) I have a DREAM! (DiffeRentially privatE smArt Metering). In: Proceedings of IH 2011, pp 118–132
Zhang Z, Qin Z, Zhu L, Weng J, Ren K (2017) Cost-friendly differential privacy for smart meters: exploiting the dual roles of the noise. IEEE Trans Smart Grid 8:619–626
Eibl G, Engel D (2017) Differential privacy for real smart metering data. Comput Sci Res Dev 32:173–182
Csiszár I, Körner J (2011) Information theory: coding theorems for discrete memoryless systems. Cambridge University Press
Nikaidô H (1954) On von Neumann’s minimax theorem. Pac J Math 4:65–72
Krishnamurthy V (2016) Partially observed markov decision processes: from filtering to controlled sensing. Cambridge University Press
Bellman R (1954) The theory of dynamic programming. Bull Am Math Soc 60:503–516
Van Erven T, Harremoës P (2014) Rényi divergence and Kullback–Leibler divergence. IEEE Trans Inf Theory 60:3797–3820
Kolter JZ, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. In: Proceedings of the SustKDD workshop on data mining applications in sustainability, pp 1–6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Li, Z., You, Y., Oechtering, T.J. (2020). Privacy Against Adversarial Hypothesis Testing: Theory and Application to Smart Meter Privacy Problem. In: Farokhi, F. (eds) Privacy in Dynamical Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0493-8_3
Download citation
DOI: https://doi.org/10.1007/978-981-15-0493-8_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0492-1
Online ISBN: 978-981-15-0493-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)