Privacy-Preserving Aggregation of Time-Series Data

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.