Title:
Efficient Oblivious Computation Techniques for Privacy-Preserving Mobile Applications
Efficient Oblivious Computation Techniques for Privacy-Preserving Mobile Applications
Author(s)
Carter, Henry
Amrutkar, Chaitrali
Dacosta, Italo
Traynor, Patrick
Amrutkar, Chaitrali
Dacosta, Italo
Traynor, Patrick
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Abstract
The growth of smartphone capability has led to
an explosion of new applications. Many of the most useful
apps use context-sensitive data, such as GPS location or social
network information. In these cases, users may not be willing
to release personal information to untrusted parties. Currently,
the solutions to performing computation on encrypted inputs
use garbled circuits combined with a variety of optimizations.
However, the capability of resource-constrained smartphones
for evaluating garbled circuits in any variation is uncertain in
practice. In [1], it is shown that certain garbled circuit evaluations
can be optimized by using homomorphic encryption.
In this paper, we take this concept to its logical extreme with
Efficient Mobile Oblivious Computation (EMOC), a technique
that completely replaces garbled circuits with homomorphic
operations on ciphertexts. We develop applications to securely
solve the millionaire’s problem, send tweets based on location,
and compute common friends in a social network, then prove
equivalent privacy guarantees to analogous constructions using
garbled circuits. We then demonstrate up to 68% runtime
reduction from the most efficient garbled circuit implementation.
In so doing, we demonstrate a practical technique
for developing privacy-preserving applications on the mobile
platform.
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Date Issued
2011
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Text
Resource Subtype
Technical Report