Abstract
Emerging approaches to network monitoring involve large numbers of agents collaborating to produce performance or security related statistics on huge, partial mesh networks. The aggregation process often involves security or business-critical information which network providers are generally unwilling to share without strong privacy protection. We present efficient and scalable protocols for privately computing a large range of aggregation functions based on addition, disjunction, and max/min. For addition, we give a protocol that is information-theoretically secure against a passive adversary, and which requires only one additional round compared to non-private protocols for computing sums. For disjunctions, we present both a computationally secure, and an information-theoretically secure solution. The latter uses a general composition approach which executes the sum protocol together with a standard multi-party protocol for a complete subgraph of “trusted servers”. This can be used, for instance, when a large network can be partitioned into a smaller number of provider domains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
The FP7 4WARD project, http://www.4ward-project.eu/
Beimel, A.: On private computation in incomplete networks. Distributed Computing 19(3), 237–252 (2007)
Ben-Or, M., Goldwasser, S., Wigderson, A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation (extended abstract). In: STOC, pp. 1–10. ACM (1988)
Bläser, M., Jakoby, A., Liskiewicz, M., Manthey, B.: Private computation: k-connected versus 1-connected networks. J. Cryptology 19(3), 341–357 (2006)
Bogdanov, D., Laur, S., Willemson, J.: Sharemind: A Framework for Fast Privacy-Preserving Computations. In: Jajodia, S., López, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192–206. Springer, Heidelberg (2008)
Brandt, F.: Efficient Cryptographic Protocol Design Based on Distributed El Gamal Encryption. In: Won, D.H., Kim, S. (eds.) ICISC 2005. LNCS, vol. 3935, pp. 32–47. Springer, Heidelberg (2006)
Burkhart, M., Strasser, M., Many, D., Dimitropoulos, X.: SEPIA: Privacy-preserving aggregation of multi-domain network events and statistics. In: 19th USENIX Security Symposium, Washington, DC, USA (August 2010)
Chan, H., Perrig, A., Song, D.X.: Secure hierarchical in-network aggregation in sensor networks. In: Juels, A., Wright, R.N., di Vimercati, S.D.C. (eds.) ACM Conference on Computer and Communications Security, pp. 278–287. ACM (2006)
Chaum, D.: The dining cryptographers problem: Unconditional sender and recipient untraceability. J. Cryptology 1(1), 65–75 (1988)
Chor, B., Kushilevitz, E.: A communication-privacy tradeoff for modular addition. Inf. Process. Lett. 45(4), 205–210 (1993)
Dam, M., Stadler, R.: A generic protocol for network state aggregation. In: Proc. Radiovetenskap Och Kommunikation, RVK (2005)
Damgård, I., Fitzi, M., Kiltz, E., Nielsen, J.B., Toft, T.: Unconditionally Secure Constant-Rounds Multi-Party Computation for Equality, Comparison, Bits and Exponentiation. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 285–304. Springer, Heidelberg (2006)
Duan, Y., Canny, J.F.: Practical private computation and zero-knowledge tools for privacy-preserving distributed data mining. In: SDM, pp. 265–276. SIAM (2008)
Franklin, M.K., Yung, M.: Secure hypergraphs: Privacy from partial broadcast. SIAM J. Discrete Math. 18(3), 437–450 (2004)
El Gamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory 31(4), 469–472 (1985)
Garofalakis, M.N., Hellerstein, J.M., Maniatis, P.: Proof sketches: Verifiable in-network aggregation. In: ICDE, pp. 996–1005. IEEE (2007)
Oded, G.: Foundations of Cryptography. Basic Applications, vol. 2. Cambridge University Press, New York (2004)
Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game or a completeness theorem for protocols with honest majority. In: STOC, pp. 218–229. ACM (1987)
He, W., Liu, X., Nguyen, H., Nahrstedt, K., Abdelzaher, T.F.: PDA: Privacy-preserving data aggregation in wireless sensor networks. In: INFOCOM, pp. 2045–2053. IEEE (2007)
Hirt, M., Maurer, U.M.: Player simulation and general adversary structures in perfect multiparty computation. J. Cryptology 13(1), 31–60 (2000)
Hu, L., Evans, D.: Secure aggregation for wireless networks. In: Workshop on Security and Assurance in Ad hoc Networks, p. 384. IEEE Computer Society (2003)
Jelasity, M., Montresor, A., Babaoglu, Ö.: Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23(3), 219–252 (2005)
Kearns, M.J.: Efficient noise-tolerant learning from statistical queries. In: STOC, pp. 392–401 (1993)
Kempe, D., Dobra, A., Gehrke, J.: Gossip-based computation of aggregate information. In: FOCS, pp. 482–491. IEEE Computer Society (2003)
Lincoln, P., Porras, P.A., Shmatikov, V.: Privacy-preserving sharing and correlation of security alerts. In: USENIX Security Symposium, pp. 239–254. USENIX (2004)
Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: A tiny aggregation service for ad-hoc sensor networks. In: OSDI (2002)
Przydatek, B., Song, D.X., Perrig, A.: SIA: secure information aggregation in sensor networks. In: Akyildiz, I.F., Estrin, D., Culler, D.E., Srivastava, M.B. (eds.) SenSys, pp. 255–265. ACM (2003)
Roughan, M., Zhang, Y.: Secure distributed data-mining and its application to large-scale network measurements. SIGCOMM Comput. Commun. Rev. 36(1), 7–14 (2006)
Slagell, A.J., Yurcik, W.: Sharing computer network logs for security and privacy: A motivation for new methodologies of anonymization. CoRR, cs.CR/0409005 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kreitz, G., Dam, M., Wikström, D. (2012). Practical Private Information Aggregation in Large Networks. In: Aura, T., Järvinen, K., Nyberg, K. (eds) Information Security Technology for Applications. NordSec 2010. Lecture Notes in Computer Science, vol 7127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27937-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-27937-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27936-2
Online ISBN: 978-3-642-27937-9
eBook Packages: Computer ScienceComputer Science (R0)