IACR News item: 13 August 2012
Erik-Oliver Blass, Guevara Noubir, Triet Vo Huu
ePrint Reportneeds to be protected. Fully homomorphic encryption is one solution
that also allows performing operations on outsourced data. However,
the involved high overhead of today\'s fully homomorphic encryption
techniques outweigh cloud cost saving advantages, rendering it
impractical. We present EPiC, a practical, efficient protocol for
the privacy-preserving evaluation of a fundamental operation on data
sets: frequency counting. In an IND-CPA encrypted outsourced data
set, a cloud user can specify a pattern, and the cloud will count
the number of occurrences of this pattern in a completely oblivious
manner. EPiC\'s main idea is, first, to reduce the problem of
counting to polynomial evaluation. Second, to efficiently evaluate
polynomials in a privacy-preserving manner, we extend previous work
on the Hidden Modular Group Order assumption and design a new
\\emph{somewhat homomorphic} encryption scheme. This scheme is highly
efficient in our particular counting scenario with a relatively
small number of possible patterns. Besides a formal analysis where
we prove EPiC\'s privacy, we also present implementation and
evaluation results. We specifically target Google\'s prominent
MapReduce paradigm as offered by major cloud providers. Our
evaluation performed both locally and in Amazon\'s public cloud with
data sets sizes of up to 1 TByte shows only modest overhead compared
to non-private counting, attesting to EPiC\'s efficiency.
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