Privacy-preserving data sharing in Smart Grid systems

The smart grid systems aim to integrate conventional power grids with modern information communication technology. While intensive research efforts have been focused on ensuring data correctness in AMI data collection and protecting data confidentiality in smart grid communications, less effort has been devoted to privacy protection in smart grid data management and sharing. In smart grid data management, the Advanced Metering Infrastructure (AMI) collects high-frequency energy consumption data, which often contains rich inhabitant and lifestyle information about the end consumers. The data is often shared with various stakeholders, such as the generators, distributors and marketers. However, the utility may not have consent of the users to share potentially sensitive data. In this paper, we develop comprehensive mechanisms to enable privacy-preserving smart data management. First, we analyze the privacy threats and consumer identifiability issues associated with high-frequency AMI data. We then present the first solution based on data sanitization, which eliminates sensitive/identifiable information before sharing usage data with external peers. Meanwhile, we present solutions based on secure multi-party computing to enable external peers to perform aggregate/statistical operations on original metering data in a privacy-preserving manner. Experiments on real-world consumption data demonstrate the validity and effectiveness of the proposed solutions.

[1]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[2]  Silvio Micali,et al.  The knowledge complexity of interactive proof-systems , 1985, STOC '85.

[3]  Ruggero G. Pensa,et al.  Pattern-Preserving k-Anonymization of Sequences and its Application to Mobil- ity Data Mining , 2008, PiLBA.

[4]  Prashant J. Shenoy,et al.  Private memoirs of a smart meter , 2010, BuildSys '10.

[5]  Bart Jacobs,et al.  Privacy-Friendly Energy-Metering via Homomorphic Encryption , 2010, STM.

[6]  Stephen B. Wicker,et al.  Inferring Personal Information from Demand-Response Systems , 2010, IEEE Security & Privacy.

[7]  Michael Zeifman,et al.  Disaggregation of home energy display data using probabilistic approach , 2012, IEEE Transactions on Consumer Electronics.

[8]  Tatsuya Yamazaki,et al.  AERO: extraction of user's activities from electric power consumption data , 2010, IEEE Transactions on Consumer Electronics.

[9]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[10]  Gang Chen,et al.  (k,P)-anonymity: towards pattern-preserving anonymity of time-series data , 2010, CIKM '10.

[11]  Georgios Kalogridis,et al.  ElecPrivacy: Evaluating the Privacy Protection of Electricity Management Algorithms , 2011, IEEE Transactions on Smart Grid.

[12]  George Danezis,et al.  Privacy-preserving smart metering , 2011, WPES '11.

[13]  Vladimir Kolesnikov,et al.  A Practical Universal Circuit Construction and Secure Evaluation of Private Functions , 2008, Financial Cryptography.

[14]  Ahmad-Reza Sadeghi,et al.  Practical Secure Evaluation of Semi-Private Functions , 2009, IACR Cryptol. ePrint Arch..

[15]  H. Vincent Poor,et al.  Smart meter privacy: A utility-privacy framework , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[16]  Yuval Ishai,et al.  Selective private function evaluation with applications to private statistics , 2001, PODC '01.

[17]  T. Elgamal A public key cryptosystem and a signature scheme based on discrete logarithms , 1984, CRYPTO 1984.

[18]  Christoph Sorge,et al.  A Privacy Model for Smart Metering , 2010, 2010 IEEE International Conference on Communications Workshops.

[19]  John R. Williams,et al.  P3: Privacy preservation protocol for appliance control application , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[20]  Taher ElGamal,et al.  A public key cyryptosystem and signature scheme based on discrete logarithms , 1985 .

[21]  Craig Gentry,et al.  Fully Homomorphic Encryption over the Integers , 2010, EUROCRYPT.

[22]  Craig Gentry,et al.  Fully homomorphic encryption using ideal lattices , 2009, STOC '09.

[23]  Chun-I Fan,et al.  Design and implementation of privacy preserving billing protocol for smart grid , 2013, The Journal of Supercomputing.

[24]  Dan Boneh,et al.  Evaluating 2-DNF Formulas on Ciphertexts , 2005, TCC.

[25]  Giacomo Verticale,et al.  A security framework for smart metering with multiple data consumers , 2012, 2012 Proceedings IEEE INFOCOM Workshops.

[26]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[27]  Marek Jawurek,et al.  Smart metering de-pseudonymization , 2011, ACSAC '11.