Advertisement

Practical Fault-Tolerant Data Aggregation

  • Krzysztof GriningEmail author
  • Marek Klonowski
  • Piotr Syga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9696)

Abstract

During Financial Cryptography 2012 Chan et al. presented a novel privacy-protection fault-tolerant data aggregation protocol. Comparing to previous work, their scheme guaranteed provable privacy of individuals and could work even if some number of users refused to participate.

In our paper we demonstrate that despite its merits, their method provides unacceptably low accuracy of aggregated data for a wide range of assumed parameters and cannot be used in majority of real-life systems. To show this we use both analytic and experimental methods.

Additionally, we present a precise data aggregation protocol that provides provable level of security even when facing massive failures of nodes. Moreover, the protocol requires significantly less computation (limited exploiting of heavy cryptography) than most of currently known fault tolerant aggregation protocols and offers better security guarantees that make it suitable for systems of limited resources (including sensor networks). To obtain our result we relax however the model and allow some limited communication between the nodes.

Keywords

Data aggregation Differential privacy Fault tolerance 

References

  1. 1.
    Shi, E., Chow, R., Hubert Chan, T-H., Song, D., Rieffel, E.: Privacy-preserving aggregation of time-series data. In: NDSS (2011)Google Scholar
  2. 2.
    Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, pp. 735–746. ACM, New York (2010)Google Scholar
  3. 3.
    Chan, T.-H.H., Shi, E., Song, D.: Privacy-preserving stream aggregation with fault tolerance. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 200–214. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Mironov, I., Pandey, O., Reingold, O., Vadhan, S.: Computational differential privacy. In: Halevi, S. (ed.) CRYPTO 2009. LNCS, vol. 5677, pp. 126–142. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Grining, K., Klonowski, M., Syga, P.: Practical fault-tolerant data aggregation. CoRR abs/1602.04138 (2016). http://arxiv.org/abs/1602.04138
  7. 7.
    Pinelis, I.: Characteristic function of the positive part of a random variable and related results, with applications. Stat. Probab. Lett. 106, 281–286 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Chan, T.H.H., Shi, E., Song, D.: Optimal lower bound for differentially private multi-party aggregation. IACR Cryptology ePrint Archive 2012 373 informal publication (2012)Google Scholar
  10. 10.
    Golle, P., Jakobsson, M., Juels, A., Syverson, P.F.: Universal re-encryption for mixnets. In: Okamoto, T. (ed.) CT-RSA 2004. LNCS, vol. 2964, pp. 163–178. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Gomułkiewicz, M., Klonowski, M., Kutyłowski, M.: Onions based on universal re-encryption – anonymous communication immune against repetitive attack. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 400–410. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Goldreich, O., Oren, Y.: Definitions and properties of zero-knowledge proof systems. J. Cryptology 7(1), 1–32 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Blum, M., Feldman, P., Micali, S.: Non-interactive zero-knowledge and its applications. In: Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, STOC 1988, pp. 103–112. ACM, New York (1988)Google Scholar
  14. 14.
    Janson, S., Luczak, T., Rucinski, A.: Random Graphs. Wiley Series in Discrete Mathematics and Optimization. Wiley, New York (2011)zbMATHGoogle Scholar
  15. 15.
    Chan, H., Perrig, A., Przydatek, B., Song, D.: Sia: Secure information aggregation in sensor networks. J. Comput. Secur. 15(1), 69–102 (2007)CrossRefGoogle Scholar
  16. 16.
    Heinzelman, W.R., Kulik, J., Balakrishnan, H.: Adaptive protocols for information dissemination in wireless sensor networks. In: Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, MobiCom 1999, pp. 174–185. ACM, New York (1999)Google Scholar
  17. 17.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: A tiny aggregation service for ad-hoc sensor networks. SIGOPS Oper. Syst. Rev. 36(SI), 131–146 (2002)CrossRefGoogle Scholar
  18. 18.
    PDA: privacy-preserving data aggregation in wireless sensor networks. In: INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE (2007)Google Scholar
  19. 19.
    He, W., Liu, X., Nguyen, H., Nahrstedt, K.: A cluster-based protocol to enforce integrity and preserve privacy in data aggregation. In: ICDCS Workshops, pp. 14–19. IEEE Computer Society (2009)Google Scholar
  20. 20.
    Roy, S., Conti, M., Setia, S., Jajodia, S.: Secure data aggregation in wireless sensor networks: Filtering out the attacker’s impact. Trans. Info. For. Sec. 9(4), 681–694 (2014)CrossRefGoogle Scholar
  21. 21.
    Papadopoulos, S., Kiayias, A., Papadias, D.: Exact in-network aggregation with integrity and confidentiality. IEEE Trans. Knowl. Data Eng. 24(10), 1760–1773 (2012)CrossRefGoogle Scholar
  22. 22.
    Feng, Y., Tang, S., Dai, G.: Fault tolerant data aggregation scheduling with local information in wireless sensor networks. Tsinghua Sci. Technol. 16(5), 451–463 (2011)CrossRefGoogle Scholar
  23. 23.
    Jhumka, A., Bradbury, M., Saginbekov, S.: Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks. J. Parallel Distrib. Comput. 74(1), 1789–1801 (2014)CrossRefzbMATHGoogle Scholar
  24. 24.
    Larrea, M., Martin, C., Astrain, J.: Hierarchical and fault-tolerant data aggregation in wireless sensor networks. In: 2nd International Symposium on Wireless Pervasive Computing, ISWPC 2007 (2007)Google Scholar
  25. 25.
    Jawurek, M., Kerschbaum, F.: Fault-tolerant privacy-preserving statistics. In: Fischer-Hübner, S., Wright, M. (eds.) PETS 2012. LNCS, vol. 7384, pp. 221–238. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  26. 26.
    Rottondi, C., Verticale, G., Krauç, C.: Distributed privacy-preserving aggregation of metering data in smart grids. IEEE J. Sel. Areas Commun. (JSAC) - JSAC Smart Grid Commun. Ser. 31(7), 1342–1354 (2013)CrossRefGoogle Scholar
  27. 27.
    Hermann.: SOTIS - a self-organizing traffic information system. In: Proceedings of the IEEE Vehicular Technology Conference Spring, pp. 2442–2246 (2003)Google Scholar
  28. 28.
    Nadeem, T., Dashtinezhad, S., Liao, C., Iftode, L.: Trafficview: Traffic data dissemination using car-to-car communication. SIGMOBILE Mob. Comput. Commun. Rev. 8(3), 6–19 (2004)CrossRefGoogle Scholar
  29. 29.
    Wischhof, L., Ebner, A., Rohling, H.: Information dissemination in Self-Organizing intervehicle networks. IEEE Trans. Intell. Transp. Syst. 6(1), 90–101 (2005)CrossRefGoogle Scholar
  30. 30.
    Caliskan, M., Graupner, D., Mauve, M.: Decentralized discovery of free parking places. In: Proceedings of the 3rd International Workshop on Vehicular Ad Hoc Networks, VANET 2006, pp. 30–39. ACM, New York (2006)Google Scholar
  31. 31.
    Antolino Rivas, D., Barceló-Ordinas, J.M., Guerrero Zapata, M., Morillo-Pozo, J.D.: Security on VANETs: Privacy, misbehaving nodes, false information and secure data aggregation. J. Netw. Comput. Appl. 34(6), 1942–1955 (2011)CrossRefGoogle Scholar
  32. 32.
    Han, Q., Du, S., Ren, D., Zhu, H.: SAS: A secure data aggregation scheme in vehicular sensing networks. In: Proceedings of IEEE International Conference on Communications, ICC 2010, Cape Town, South Africa, pp. 23–27. IEEE ,1–5 May 2010Google Scholar
  33. 33.
    Mohanty, S., Jena, D.: Secure data aggregation in vehicular-adhoc networks: A survey. Procedia Technol. 6, 922–929 (2012). 2nd International Conference on Communication, Computing and Security [ICCCS-2012]CrossRefGoogle Scholar
  34. 34.
    Benaloh, J.C.: Secret sharing homomorphisms: keeping shares of a secret secret. In: Odlyzko, A.M. (ed.) CRYPTO 1986. LNCS, vol. 263, pp. 251–260. Springer, Heidelberg (1987)CrossRefGoogle Scholar
  35. 35.
    Beimel, A.: Secret-sharing schemes: a survey. In: Chee, Y.M., Guo, Z., Ling, S., Shao, F., Tang, Y., Wang, H., Xing, C. (eds.) IWCC 2011. LNCS, vol. 6639, pp. 11–46. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Krzysztof Grining
    • 1
    Email author
  • Marek Klonowski
    • 1
  • Piotr Syga
    • 1
  1. 1.Faculty of Fundamental Problems of TechnologyWrocław University of TechnologyWrocławPoland

Personalised recommendations