Abstract
We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Our protocols are scalable in settings where the volume of streaming data is large, since we guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator.
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References
Arasu, A., Manku, G.S.: Approximate counts and quantiles over sliding windows. In: PODS (2004)
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)
Chan, H.-L., Lam, T.-W., Lee, L.-K., Ting, H.-F.: Continuous monitoring of distributed data streams over a time-based sliding window. In: STACS (2010)
Chan, T.-H.H., Li, M., Shi, E., Xu, W.: Differentially private continual monitoring of heavy hitters from distributed streams. In: Cryptology ePrint Archive (2012)
Hubert Chan, T.-H., Shi, E., Song, D.: Private and Continual Release of Statistics. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds.) ICALP 2010, Part II. LNCS, vol. 6199, pp. 405–417. Springer, Heidelberg (2010)
Demaine, E.D., López-Ortiz, A., Munro, J.I.J.: Frequency Estimation of Internet Packet Streams with Limited Space. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 348–360. Springer, Heidelberg (2002)
Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006, Part II. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)
Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential privacy under continual observation. In: STOC, pp. 715–724 (2010)
Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N., Yekhanin, S.: Pan-private streaming algorithms. In: ICS, pp. 66–80 (2010)
Ganta, S.R., Kasiviswanathan, S.P., Smith, A.: Composition attacks and auxiliary information in data privacy. In: KDD, pp. 265–273 (2008)
Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. In: STOC (2009)
Goldreich, O.: The Foundations of Cryptography - Volume 2, Basic Applications. Cambridge University Press (2004)
Karp, R.M., Shenker, S., Papadimitriou, C.H.: A simple algorithm for finding frequent elements in streams and bags. ACM Trans. Database Syst. 28, 51–55 (2003)
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)
Lee, L.K., Ting, H.F.: A simpler and more efficient deterministic scheme for finding frequent items over sliding windows. In: PODS (2006)
McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD Conference, pp. 19–30 (2009)
Mir, D.J., Muthukrishnan, S., Nikolov, A., Wright, R.N.: Pan-private algorithms via statistics on sketches. In: PODS, pp. 37–48 (2011)
Misra, J., Gries, D.: Finding repeated elements. Sci. Comput. Program. 2(2), 143–152 (1982)
Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: SIGMOD 2010, pp. 735–746 (2010)
Shi, E., Chan, H., Rieffel, E., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: NDSS (2011)
Yi, K., Zhang, Q.: Optimal tracking of distributed heavy hitters and quantiles. In: PODS (2009)
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Chan, T.H.H., Li, M., Shi, E., Xu, W. (2012). Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams. In: Fischer-Hübner, S., Wright, M. (eds) Privacy Enhancing Technologies. PETS 2012. Lecture Notes in Computer Science, vol 7384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31680-7_8
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DOI: https://doi.org/10.1007/978-3-642-31680-7_8
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