CRMar 24, 2017

k-Anonymously Private Search over Encrypted Data

arXiv:1703.08269v11 citationsHas Code
Originality Synthesis-oriented
AI Analysis

This work addresses privacy-preserving search for users handling sensitive data, but it is incremental as it benchmarks existing methods.

The paper compared homomorphic encryption methods for private search with k-anonymity, finding that Goldwasser-Micali encryption offers practical performance while fully homomorphic encryptions are significantly slower.

In this paper we compare the performance of various homomorphic encryption methods on a private search scheme that can achieve $k$-anonymity privacy. To make our benchmarking fair, we use open sourced cryptographic libraries which are written by experts and well scrutinized. We find that Goldwasser-Micali encryption achieves good enough performance for practical use, whereas fully homomorphic encryptions are much slower than partial ones like Goldwasser-Micali and Paillier.

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