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

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<sub<i</sub<,y<sub<i</sub<), where i=1, ..., n , how can the...
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Autor*in:	Yan Shao [verfasserIn] Wenjing Hong [verfasserIn] Zhanjun Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Privacy-preserving distributed secure ratio data-mining secure multi-party computation

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 20756-20766
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:20756-20766

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2894682

Katalog-ID:	DOAJ007574142

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520			\|a 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<sub<i</sub<,y<sub<i</sub<), where i=1, ..., n , how can they get the value of Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub</ Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< without disclosing (x<sub<i</sub<,y<sub<i</sub<), and Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub< and Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< 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.
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callnumber-first	T - Technology
author	Yan Shao
spellingShingle	Yan Shao misc TK1-9971 misc Privacy-preserving misc distributed misc secure misc ratio misc data-mining misc secure multi-party computation misc Electrical engineering. Electronics. Nuclear engineering A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining
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topic_title	TK1-9971 A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining Privacy-preserving distributed secure ratio data-mining secure multi-party computation
topic	misc TK1-9971 misc Privacy-preserving misc distributed misc secure misc ratio misc data-mining misc secure multi-party computation misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Privacy-preserving misc distributed misc secure misc ratio misc data-mining misc secure multi-party computation misc Electrical engineering. Electronics. Nuclear engineering
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title	A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining
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title_sort	new method to compute ratio of secure summations and its application in privacy preserving distributed data mining
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title_auth	A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining
abstract	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<sub<i</sub<,y<sub<i</sub<), where i=1, ..., n , how can they get the value of Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub</ Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< without disclosing (x<sub<i</sub<,y<sub<i</sub<), and Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub< and Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< 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.
abstractGer	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<sub<i</sub<,y<sub<i</sub<), where i=1, ..., n , how can they get the value of Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub</ Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< without disclosing (x<sub<i</sub<,y<sub<i</sub<), and Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub< and Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< 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.
abstract_unstemmed	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<sub<i</sub<,y<sub<i</sub<), where i=1, ..., n , how can they get the value of Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub</ Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< without disclosing (x<sub<i</sub<,y<sub<i</sub<), and Σ<sub<i=1</sub<<sup<n</sup< x<sub<i</sub< and Σ<sub<i=1</sub<<sup<n</sup< y<sub<i</sub< 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.
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title_short	A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining
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