@conference {19019,
	title = {Privacy-preserving aggregation of time-series data},
	volume = {17},
	year = {2011},
	month = {2011},
	abstract = {We consider how an untrusted data aggregator canlearn desired statistics over multiple participants{\textquoteright} data,
without compromising each individual{\textquoteright}s privacy. We
propose a construction that allows a group of partici-
pants to periodically upload encrypted values to a data
aggregator, such that the aggregator is able to compute
the sum of all participants{\textquoteright} 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.
},
	url = {http://www.eecs.berkeley.edu/~elaines/docs/ndss2011.pdf},
	author = {Elaine Shi and Chan, T. and Rieffel, E. and Chow, R. and Song,D.}
}
