(1982). Nearness Relations Among Measures of Central Tendency and Dispersion: Part 1. The Two-Year College Mathematics Journal: Vol. 13, No. 5, pp. 315-327.

Nearness Relations Among Measures of Central Tendency and Dispersion: Part 1

In this paper, we propose a practically efficient model for securely computing rank-based statistics, e.g., median, percentiles and quartiles, over distributed datasets in the malicious setting without leaking individual data privacy. Based on the binary search technique of Aggarwal et al. (EUROCRYPT \textquotesingle 04), we respectively present an interactive protocol and a non-interactive protocol, involving at most $\log ||R||$ rounds, where $||R||$ is the range size of the dataset elements. Besides, we introduce a series of optimisation techniques to reduce the round complexity. Our computing model is modular and can be instantiated with either homomorphic encryption or secret-sharing schemes. Compared to the state-of-the-art solutions, it provides stronger security and privacy while maintaining high efficiency and accuracy. Unlike differential-privacy-based solutions, it does not suffer a trade-off between accuracy and privacy. On the other hand, it only involves $O(N \log ||R||)$ time complexity, which is far more efficient than those bitwise-comparison-based solutions with $O(N^2\log ||R||)$ time complexity, where $N$ is the dataset size. Finally, we provide a UC-secure instantiation with the threshold Paillier cryptosystem and $\Sigma$-protocol zero-knowledge proofs of knowledge.

pdf/practically-efficient-secure-computation-of-rank-based-3gcaiewf.pdf

Practically Efficient Secure Computation of Rank-based Statistics Over Distributed Datasets

How Close Are the Mean and the Median

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How Close Are the Mean and the Median