Abstract
We consider applications where an untrusted aggregator would like to collect privacy sensitive data from users, and compute aggregate statistics periodically. For example, imagine a smart grid operator who wishes to aggregate the total power consumption of a neighborhood every ten minutes; or a market researcher who wishes to track the fraction of population watching ESPN on an hourly basis.
We design novel mechanisms that allow an aggregator to accurately estimate such statistics, while offering provable guarantees of user privacy against the untrusted aggregator. Our constructions are resilient to user failure and compromise, and can efficiently support dynamic joins and leaves. Our constructions also exemplify the clear advantage of combining applied cryptography and differential privacy techniques.
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References
Blum, A., Ligett, K., Roth, A.: A learning theory approach to non-interactive database privacy. In: STOC (2008)
Chan, H., Shi, E., Song, D.: Privacy-preserving stream aggregation with fault tolerance. Full online technical report (2011), http://eprint.iacr.org/2011/722.pdf
Chan, H., Shi, E., Song, D.: Tight lower bounds for distributed private data analysis (2011) (submission)
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. LNCS, vol. 6199, pp. 405–417. Springer, Heidelberg (2010)
Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Dwork, C.: A firm foundation for private data analysis. Communications of the ACM (2010)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our Data, Ourselves: Privacy Via Distributed Noise Generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006)
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)
Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential privacy under continual observation. In: STOC (2010)
Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. In: STOC (2009)
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)
Przydatek, B., Song, D., Perrig, A.: Sia: secure information aggregation in sensor networks. In: ACM Sensys (2003)
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)
Whitney, L.: Microsoft urges laws to boost trust in the cloud, http://news.cnet.com/8301-1009_3-10437844-83.html
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Chan, T.H.H., Shi, E., Song, D. (2012). Privacy-Preserving Stream Aggregation with Fault Tolerance. In: Keromytis, A.D. (eds) Financial Cryptography and Data Security. FC 2012. Lecture Notes in Computer Science, vol 7397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32946-3_15
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DOI: https://doi.org/10.1007/978-3-642-32946-3_15
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