Private Data Aggregation over Selected Subsets of Users

  • Amit DattaEmail author
  • Marc JoyeEmail author
  • Nadia Fawaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11829)


Aggregator-oblivious (\(\mathsf {AO}\)) encryption allows the computation of aggregate statistics over sensitive data by an untrusted party, called aggregator. In this paper, we focus on exact aggregation, wherein the aggregator obtains the exact sum over the participants. We identify three major drawbacks for existing exact \(\mathsf {AO}\) encryption schemes—no support for dynamic groups of users, the requirement of additional trusted third parties, and the need of additional communication channels among users. We present privacy-preserving aggregation schemes that do not require any third-party or communication channels among users and are exact and dynamic. The performance of our schemes is evaluated by presenting running times.


Data aggregation Privacy Aggregator obliviousness 


  1. 1.
    Ács, G., Castelluccia, C.: I have a DREAM! (DiffeRentially privatE smArt Metering). In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 118–132. Springer, Heidelberg (2011). Scholar
  2. 2.
    Barthe, G., Danezis, G., Grégoire, B., Kunz, C., Zanella-Béguelin, S.: Verified computational differential privacy with applications to smart metering. In: 26th IEEE Computer Security Foundations Symposium (CSF 2013), pp. 287–301. IEEE Press (2013).
  3. 3.
    Benhamouda, F., Joye, M., Libert, B.: A new framework for privacy-preserving aggregation of time-series data. ACM Trans. Inf. Syst. Secur. 18(3) (2016). Scholar
  4. 4.
    Boneh, D.: The decision Diffie-Hellman problem. In: Buhler, J.P. (ed.) ANTS 1998. LNCS, vol. 1423, pp. 48–63. Springer, Heidelberg (1998). Scholar
  5. 5.
    Court of Justice of the European Union: The court of justice declares that the commission’s US safe harbour decision is invalid. Press Release No 117/15, Judgment in Case C-362/14 Maximillian Schrems v Data Protection Commissioner, October 2015.
  6. 6.
    Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7(1), 1–26 (1979). Scholar
  7. 7.
    Erkin, Z., Tsudik, G.: Private computation of spatial and temporal power consumption with smart meters. In: Bao, F., Samarati, P., Zhou, J. (eds.) ACNS 2012. LNCS, vol. 7341, pp. 561–577. Springer, Heidelberg (2012). Scholar
  8. 8.
    Garcia, F.D., Jacobs, B.: Privacy-friendly energy-metering via homomorphic encryption. In: Cuellar, J., Lopez, J., Barthe, G., Pretschner, A. (eds.) STM 2010. LNCS, vol. 6710, pp. 226–238. Springer, Heidelberg (2011). Scholar
  9. 9.
    Hao, F., Zieliński, P.: A 2-round anonymous veto protocol. In: Christianson, B., Crispo, B., Malcolm, J.A., Roe, M. (eds.) Security Protocols 2006. LNCS, vol. 5087, pp. 202–211. Springer, Heidelberg (2009). Scholar
  10. 10.
    Hellman, M.E., Diffie, W.: New directions in cryptography. IEEE Trans. Inf. Theory 22(6), 644–654 (1976). Scholar
  11. 11.
    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). Scholar
  12. 12.
    Jawurek, M., Kerschbaum, F., Danezis, G.: SoK: privacy technologies for smart grids - a survey of options. Technical report MSR-TR-2012-119, Microsoft Research, Cambridge, UK, November 2012.
  13. 13.
    Joye, M., Libert, B.: A scalable scheme for privacy-preserving aggregation of time-series data. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 111–125. Springer, Heidelberg (2013). Scholar
  14. 14.
    Kursawe, K., Danezis, G., Kohlweiss, M.: Privacy-friendly aggregation for the smart-grid. In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 175–191. Springer, Heidelberg (2011). Scholar
  15. 15.
    Leontiadis, I., Elkhiyaoui, K., Molva, R.: Private and dynamic time-series data aggregation with trust relaxation. In: Gritzalis, D., Kiayias, A., Askoxylakis, I. (eds.) CANS 2014. LNCS, vol. 8813, pp. 305–320. Springer, Cham (2014). Scholar
  16. 16.
    Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: Elmagarmid, A.K., Agrawal, D. (eds.) 2010 ACM SIGMOD International Conference on Management of Data, pp. 735–746. ACM Press (2010).
  17. 17.
    Shi, E., Chan, T.H.H., Rieffel, E.G., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: Network and Distributed System Security Symposium (NDSS 2011). The Internet Society (2011).
  18. 18.
    Teruya, T., Saito, K., Kanayama, N., Kawahara, Y., Kobayashi, T., Okamoto, E.: Constructing symmetric pairings over supersingular elliptic curves with embedding degree three. In: Cao, Z., Zhang, F. (eds.) Pairing 2013. LNCS, vol. 8365, pp. 305–320. Springer, Cham (2014). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Snap Inc.Santa MonicaUSA
  2. 2.OneSpanBrusselsBelgium

Personalised recommendations