Secure Data Obfuscation Scheme to Enable Privacy-Preserving State Estimation in Smart Grid AMI Networks


TONYALI S., Cakmak O., Akkaya K., Mahmoud M. M. E. A., Guvenc I.

IEEE INTERNET OF THINGS JOURNAL, cilt.3, sa.5, ss.709-719, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3 Sayı: 5
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1109/jiot.2015.2510504
  • Dergi Adı: IEEE INTERNET OF THINGS JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.709-719
  • Anahtar Kelimeler: Advanced metering infrastructure (AMI), data obfuscation, distribution state estimation, privacy preservation, smart grid (SG), AGGREGATION
  • Abdullah Gül Üniversitesi Adresli: Hayır

Özet

While the newly envisioned smart(er) grid (SG) will result in a more efficient and reliable power grid, its collection and use of fine-grained meter data has widely raised concerns on consumer privacy. While a number of approaches are available for preserving consumer privacy, these approaches are mostly not very practical to be used due to two reasons. 1) Since the data is hidden, this reduces the ability of the utility company to use the data for distribution state estimation. 2) The approaches were not tested under realistic wireless infrastructures that are currently in use. In this paper, we propose to implement a meter data obfuscation approach to preserve consumer privacy that has the ability to perform distribution state estimation. We then assess its performance on a large-scale advanced metering infrastructure (AMI) network built upon the new IEEE 802.11s wireless mesh standard. For the data obfuscation approach, we propose two secure obfuscation value distribution mechanisms on this 802.11s-based wireless mesh network (WMN). Using obfuscation values provided via this approach, the meter readings are obfuscated to protect consumer privacy from eavesdroppers and the utility companies while preserving the utility companies' ability to use the data for state estimation. We assessed the impact of this approach on data goodput, delay, and packet delivery ratio (PDR) under a variety of conditions. Simulation results have shown that the proposed approach can provide very similar performance to that of non-privacy approach with negligible overheads on the meters and network.