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Privacy-Preserving Stream Aggregation with Fault Tolerance

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Financial Cryptography and Data Security (FC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 7397))

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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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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32945-6

  • Online ISBN: 978-3-642-32946-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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