Implementation of privacy-friendly aggregation for the smart grid

In recent years a number of protocols have been suggested toward privacy-preserving aggregation of smart meter data, allowing electricity network operators to perform a large part of grid maintenance and administrative operations without having to touch any privacy-sensitive data. In light of upcoming European legislation, this approach has gained quite some attention. However, to allow such protocols to have a chance to make it into a real system, it is vital to add credibility by demonstrating that the approach scales, is reasonably robust, and can be integrated into the existing and planned smart metering chains. This paper presents results from integration and scalability tests performed on 100 DLMS/COSEM smart meters in collaboration with a meter manufacturer and a Dutch utility. We outline the use cases, lessons learned, and choices that had to be made to allow the protocols to run in a real system, as well as some privacy challenges that cannot be covered by this technology.

[1]  Peng Liu,et al.  Secure and privacy-preserving information aggregation for smart grids , 2011, Int. J. Secur. Networks.

[2]  Zekeriya Erkin,et al.  Private Computation of Spatial and Temporal Power Consumption with Smart Meters , 2012, ACNS.

[3]  Colette Cuijpers,et al.  Het wetsvoorstel 'slimme meters' : Een privacytoets op basis van art. 8 EVRM , 2008 .

[4]  Xiaohui Liang,et al.  EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications , 2012, IEEE Transactions on Parallel and Distributed Systems.

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

[6]  George Danezis,et al.  Privacy-Friendly Aggregation for the Smart-Grid , 2011, PETS.

[7]  Peng Liu,et al.  Secure Information Aggregation for Smart Grids Using Homomorphic Encryption , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[8]  Giacomo Verticale,et al.  Implementation of a protocol for secure distributed aggregation of smart metering data , 2012, 2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP).

[9]  Prashant J. Shenoy,et al.  Designing Privacy-Preserving Smart Meters with Low-Cost Microcontrollers , 2012, Financial Cryptography.

[10]  George Danezis,et al.  Differentially Private Billing with Rebates , 2011 .

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

[12]  John R. Williams,et al.  Efficient authentication scheme for data aggregation in smart grid with fault tolerance and fault diagnosis , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[13]  Fernando Pérez-González,et al.  Privacy-preserving data aggregation in smart metering systems: an overview , 2013, IEEE Signal Processing Magazine.

[14]  R. L. Lagendijk,et al.  An Overview of Privacy-Preserving Data Aggregation in Smart Metering Systems , 2012 .

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