Brief Announcement: Privacy Preserving Mining of Distributed Data Using a Trusted and Partitioned Third Party

Maoz, Nir; Gudes, Ehud

doi:10.1007/978-3-319-60080-2_14

Cited by 1 publication

(1 citation statement)

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“…Secure multi-party computation (SMC) [17], [25], [27] is an important approach for privacy preserving distributed data mining (PPDDM) [3], [13], [14], [29], [30]. Based on SMC, different data owners can implement computation of useful aggregate statistics over the entire dataset without revealing additional information.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining

Shao

Hong

2019

IEEE Access

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Computing the ratio of secure summations (RSS) is one of the most important tools for privacy preserving distributed data mining. It refers to such a problem; given the n parties and their respective secret values (x i ,y i ), where i = 1, . . . , n, how can they get the value of n i=1 x i / n i=1 y i without disclosing (x i ,y i ), n i=1 x i and n i=1 y i ? In this paper, we propose a new method to solve this problem. Compared with the existing methods, our method is not only secure under the assumption of semi-honest but also can resist collusion attacks-even when all but one party are corrupt. At the same time, the computational complexity and communication complexity of our method are both O(n). Therefore, it can meet the needs of the practical application in terms of both security and efficiency. In addition to the theoretical guarantee, the practical usability of our method is also verified through experiments by constructing Naive Bayes classifiers in a privacy-preserving distributed environment. 20756

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