Private Computation of Spatial and Temporal Power Consumption with Smart Meters
Smart metering of utility consumption is rapidly becoming reality for multitudes of people and households. It promises real-time measurement and adjustment of power demand which is expected to result in lower overall energy use and better load balancing. On the other hand, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including all kinds of personal attributes and activities. Reconciling smart metering’s benefits with privacy concerns is a major challenge.
In this paper we explore some simple and relatively efficient cryptographic privacy techniques that allow spatial (group-wide) aggregation of smart meter measurements. We also consider temporal aggregation of multiple measurements for a single smart meter. While our work is certainly not the first to tackle this topic, we believe that proposed techniques are appealing due to their simplicity, few assumptions and peer-based nature, i.e., no need for any on-line aggregators or trusted third parties.
KeywordsWireless Sensor Network Total Consumption Secret Sharing Aggregate Consumption Homomorphic Encryption
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- 2.Anderson, R., Fuloria, S.: On the security economics of electricity metering. In: The 9th Workshop on the Economics of Information Security (2010)Google Scholar
- 5.Castelluccia, C., Chan, A.C.-F., Mykletun, E., Tsudik, G.: Efficient and provably secure aggregation of encrypted data in wireless sensor networks. ACM Trans. Sen. Netw. 5, 20:1–20:36 (2009)Google Scholar
- 6.Castelluccia, C., Chan, A.C.-F., Mykletun, E., Tsudik, G.: Efficient and provably secure aggregation of encrypted data in wireless sensor networks. TOSN 5(3) (2009)Google Scholar
- 7.Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient aggregation of encrypted data in wireless sensor networks. In: Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 109–117. IEEE Computer Society, Washington, DC (2005)CrossRefGoogle Scholar
- 9.Doraswamy, N., Harkins, D.: IPSec: The New Security Standard for the Internet, Intranets, and Virtual Private Networks. Prentice Hall PTR, Upper Saddle River (1999)Google Scholar
- 10.Fine, J.: Malfunctioning smart meters demonstrate their intelligence (May 16, 2011), http://blogs.edf.org/energyexchange/2011/05/16/malfunctioning-smart-meters-demonstrate-their-intelligence/
- 11.Fontaine, C., Galand, F.: A survey of homomorphic encryption for nonspecialists. EURASIP Journal on Information Security (2007)Google Scholar
- 13.Goldreich, O.: Foundations of Cryptography. Basic Applications, 1st edn., vol. 2. Cambridge University Press (May 2004) ISBN 0-521-83084-2Google Scholar
- 16.Molina-Markham, A., Danezis, G., Fu, K., Shenoy, P.J., Irwin, D.E.: Designing privacy-preserving smart meters with low-cost microcontrollers. IACR Cryptology ePrint Archive 2011, 544 (2011)Google Scholar
- 17.Paillier, P.: Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)Google Scholar
- 18.Peter, S., Piotrowski, K., Langendoerfer, P.: On concealed data aggregation for wireless sensor networks. In: 4th IEEE Consumer Communications and Networking Conferences (2007)Google Scholar
- 20.Shi, E., Chan, T.-H.H., Rieffel, E.G., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: Proceedings of 18th Annual Network and Distributed System Security Symposium (NDSS 2011) (February 2011)Google Scholar
- 21.Troncoso-Pastoriza, J.R., Katzenbeisser, S., Celik, M.U., Lemma, A.N.: A secure multidimensional point inclusion protocol. In: ACM Workshop on Multimedia and Security, pp. 109–120 (2007)Google Scholar