End-User Data Privacy

  • Kianoosh G. Boroojeni
  • M. Hadi Amini
  • S. S. Iyengar


Energy systems are undergoing enormous transformations around the world. Though loosely defined, the concept of the smart grid entails power networks transmitting digital information as well as energy. The primary purpose is to allow (near) real-time consumption and generation data to be transmitted between different nodes, but it also allows for possibilities such as remote activation of appliances. In combination with facilitating increased amounts of distributed generation, often from renewable sources with variable output, the goal is to optimize the balance of generation and consumption in order to achieve efficiencies. Chapter  6 addresses the location privacy concerns that end-users (e.g., smart meters) would have when they use a variety of location-based services on which the smart grid controllers rely for their main functionalities. Section 6.1 introduces the end-users like smart meters and their importance in a given smart grid. Section 6.2 introduces the preliminary concepts of location privacy and the localization attacks. Section 6.3 introduces a couple of location privacy preservation mechanisms as countermeasures to the localization attack. At the end, a summary and outlook of Chap.  6 will be presented.


Mobile Node Smart Grid Location Privacy Query Message Localization Attack 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    R. Dewri, Location privacy and attacker knowledge: who are we fighting against? in Security and Privacy in Communication Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 96 (Springer, Berlin, 2012), pp. 96–115Google Scholar
  2. 2.
    M. Li, S. Salinas, A. Thapa, P, Li, n-CD: a geometric approach to preserving location privacy in location-based services, in Proceedings of IEEE INFOCOM, 2013Google Scholar
  3. 3.
    M. Gruteser, D. Grunwald, Anonymous usage of location-based services through spatial and temporal cloaking, in ACM Mobisys’03, May 2003Google Scholar
  4. 4.
    B. Gedik, L. Liu, Protecting location privacy with personalized k-anonymity: architecture and algorithms. IEEE Trans. Mob. Comput. 7 (1), 1–18 (2008)CrossRefGoogle Scholar
  5. 5.
    J. Meyerowitz, R.R. Choudhury, Hiding stars with fireworks: location privacy through camouflage, in Proceedings of ACM MobiCom, Beijing, Sept 2009Google Scholar
  6. 6.
    M.F. Mokbel, C.Y. Chow, W.G. Aref, The new casper: query processing for location services without compromising privacy, in Proceedings of VLDB, 2006Google Scholar
  7. 7.
    P. Kalnis, G. Ghinita, K. Mouratidis, D. Papadias, Preventing location-based identity inference in anonymous spatial queries. IEEE Trans. Knowl. Data Eng. 19 (12), 1719–1733 (2007)CrossRefGoogle Scholar
  8. 8.
    B. Gedik, L. Liu, Location privacy in mobile systems: a personalized anonymization model, in Proceedings of IEEE ICDCS, Columbus, OH, June 2005Google Scholar
  9. 9.
    C.-Y. Chow, M.F. Mokbel, X. Liu, A peer-to-peer spatial cloaking algorithm for anonymous location-based service, in Proceedings of ACM GIS, Arlington, VA, Nov 2006Google Scholar
  10. 10.
    A. Beresford, F. Stajano, Location privacy in pervasive computing. IEEE Pervasive Comput. 2 (1), 46–55 (2003)CrossRefGoogle Scholar
  11. 11.
    B. Hoh, M. Gruteser, H. Xiong, A. Alrabady, Preserving privacy in GPS traces via uncertainty-aware path cloaking, in Proceedings of ACM CCS 2007, Alexandria, VA, Jan 2007Google Scholar
  12. 12.
    H. Kido, Y. Yanagisawa, T. Satoh, An anonymous communication technique using dummies for location-based services, in Proceedings of IEEE ICPS, Santorini, July 2006Google Scholar
  13. 13.
    H. Lu, C.S. Jensen, M.L. Yiu, Pad: privacy-area aware, dummy based location privacy in mobile services, in Proceedings of ACM MobiDE, Vancouver, June 2008Google Scholar
  14. 14.
    M. Duckham, L. Kulik, A formal model of obfuscation and negotiation for location privacy, in Proceedings of International Conference on Pervasive Computing, Munich, May 2005Google Scholar
  15. 15.
    C.A. Ardagna, M. Cremonini, S.D.C. di Vimercati, P. Samarati, An obfuscation-based approach for protecting location privacy. IEEE Trans. Dependable Secure Comput. 8 (1), 13–27 (2011)CrossRefGoogle Scholar
  16. 16.
    A. Pingley, W. Yu, N. Zhang, X. Fu, W. Zhao, Cap: a contextaware privacy protection system for location-based services, in Proceedings of IEEE ICDCS, Montreal, June 2009Google Scholar
  17. 17.
    M. Damiani, E. Bertino, C. Silvestri, Probe: an obfuscation system for the protection of sensitive location information in LBS. Technical Report 2001–145, CERIAS, 2008Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Kianoosh G. Boroojeni
    • 1
  • M. Hadi Amini
    • 2
    • 3
  • S. S. Iyengar
    • 1
  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.SYSU-CMU Joint Institute of Engineering School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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