A Distributed Privacy-Aware Architecture for Communication in Smart Grids

The recent introduction of the smart grids poses the need for a distributed communication infrastructure able to efficiently transmit the energy measurements collected by the smart meters, while simultaneously coping with several privacy and security constraints. In this work we introduce a novel communication architecture able to efficiently solve such a problem. Our proposal is based on a heterogeneous architecture that makes use of functional nodes (namely the privacy peers) that are interposed between the users and the utility server. The proposed architecture is able to deal with both the need of anonymizing the measurement data (by implementing a Secure Multiparty Computation method) and of simultaneously attributing these data to the users for billing purposes. Moreover, we also show that our architecture is robust to both semi-honest and malicious adversaries.

[1]  Josh Benaloh,et al.  Secret Sharing Homomorphisms: Keeping Shares of A Secret Sharing , 1986, CRYPTO.

[2]  Yehuda Lindell,et al.  Secure Multiparty Computation for Privacy-Preserving Data Mining , 2009, IACR Cryptol. ePrint Arch..

[3]  Yitao Duan,et al.  P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users , 2010, USENIX Security Symposium.

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

[5]  Annabelle Lee,et al.  Guidelines for Smart Grid Cyber Security , 2010 .

[6]  David Chaum,et al.  Multiparty unconditionally secure protocols , 1988, STOC '88.

[7]  Ronald Cramer,et al.  Introduction to Secure Computation , 1998, Lectures on Data Security.

[8]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[9]  Torben P. Pedersen Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing , 1991, CRYPTO.

[10]  Yitao Duan,et al.  Efficient Privacy-Preserving Association Rule Mining: P4P Style , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[11]  David Chaum,et al.  Multiparty Computations Ensuring Privacy of Each Party's Input and Correctness of the Result , 1987, CRYPTO.

[12]  Georgios Kalogridis,et al.  Smart Grid Privacy via Anonymization of Smart Metering Data , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[13]  Mihir Bellare,et al.  On Defining Proofs of Knowledge , 1992, CRYPTO.

[14]  Yitao Duan,et al.  Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining , 2008, SDM.

[15]  Ivan Damgård,et al.  Zero-Knowledge Proofs for Finite Field Arithmetic; or: Can Zero-Knowledge be for Free? , 1998, CRYPTO.

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

[17]  Yitao Duan,et al.  Practical private computation of vector addition-based functions , 2007, PODC '07.

[18]  Ivan Damgård,et al.  Commitment Schemes and Zero-Knowledge Protocols , 1998, Lectures on Data Security.

[19]  Josh Benaloh,et al.  Secret Sharing Homomorphisms: Keeping Shares of A Secret Sharing , 1986, CRYPTO.

[20]  Avi Wigderson,et al.  Completeness theorems for non-cryptographic fault-tolerant distributed computation , 1988, STOC '88.

[21]  Ran Canetti,et al.  Universally composable security: a new paradigm for cryptographic protocols , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

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

[23]  Paul Feldman,et al.  A practical scheme for non-interactive verifiable secret sharing , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[24]  Baruch Awerbuch,et al.  Verifiable secret sharing and achieving simultaneity in the presence of faults , 1985, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

[25]  E. Quinn Privacy and the New Energy Infrastructure , 2009 .