Privacy-preserving aggregation of time-series data

Privacy-preserving aggregation of time-series data (2011)

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by Elaine Shi , Richard Chow , T-h. Hubert Chan , Dawn Song , Eleanor Rieffel

Venue:	In NDSS
Citations:	63 - 5 self

BibTeX

@INPROCEEDINGS{Shi11privacy-preservingaggregation,
    author = {Elaine Shi and Richard Chow and T-h. Hubert Chan and Dawn Song and Eleanor Rieffel},
    title = {Privacy-preserving aggregation of time-series data},
    booktitle = {In NDSS},
    year = {2011}
}

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Abstract

We consider how an untrusted data aggregator can learn desired statistics over multiple participants ’ data, without compromising each individual’s privacy. We propose a construction that allows a group of participants to periodically upload encrypted values to a data aggregator, such that the aggregator is able to compute the sum of all participants ’ values in every time period, but is unable to learn anything else. We achieve strong privacy guarantees using two main techniques. First, we show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys. Second, we describe a distributed data randomization procedure that guarantees the differential privacy of the outcome statistic, even when a subset of participants might be compromised. 1

Keyphrases

time-series data privacy-preserving aggregation outcome statistic main technique untrusted data aggregator participant value multiple participant data applied cryptographic technique time period data randomization procedure individual privacy data aggregator differential privacy desired statistic strong privacy guarantee multiple ciphertexts different user key