Two Schemes of Privacy-Preserving Trust Evaluation

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Two Schemes of Privacy-Preserving Trust Evaluation. / Yan, Zheng; Ding, Wenxiu; Niemi, Valtteri; Vasilakos, Athanasios V.

julkaisussa: Future Generation Computer Systems: the international journal of grid computing and escience, Vuosikerta 62, 2016, s. 175–189.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Yan, Zheng ; Ding, Wenxiu ; Niemi, Valtteri ; Vasilakos, Athanasios V. / Two Schemes of Privacy-Preserving Trust Evaluation. Julkaisussa: Future Generation Computer Systems: the international journal of grid computing and escience. 2016 ; Vuosikerta 62. Sivut 175–189.

Bibtex - Lataa

@article{b3eb3577ac4e4b40b2f7f2f258eafbd0,
title = "Two Schemes of Privacy-Preserving Trust Evaluation",
abstract = "Trust evaluation computes trust values by collecting and processing trust evidence. It plays an important role in trust management that automatically ensures trust relationships among system entities and enhances system security. But trust evidence collection and process may cause privacy leakage, which makes involved entities reluctant to provide personal evidence that is essential for trust evaluation. Current literature pays little attention to Privacy-Preserving Trust Evaluation (PPTE). Existing work still has many limitations, especially on generality, efficiency and reliability. In this paper, we propose two practical schemes to guard privacy of trust evidence providers based on additive homomorphic encryption in order to support a traditional class of trust evaluation that contains evidence summation. The first scheme achieves better computational efficiency, while the second one provides greater security at the expense of a higher computational cost. Accordingly, two trust evaluation algorithms are further proposed to flexibly support different application cases. Specifically, these algorithms can overcome attacks raised by internal malicious evidence providers to some extent even though the trust evaluation is partially performed in an encrypted form. Extensive analysis and performance evaluation show the security and effectivity of our schemes for potential application prospect and their efficiency to support big data process.",
keywords = "Big data, Homomorphic encryption, Privacy preservation, Secure multiparty computation, Trust evaluation",
author = "Zheng Yan and Wenxiu Ding and Valtteri Niemi and Vasilakos, {Athanasios V.}",
year = "2016",
doi = "10.1016/j.future.2015.11.006",
language = "English",
volume = "62",
pages = "175–189",
journal = "Future Generation Computer Systems: the international journal of grid computing and escience",
issn = "0167-739X",

}

RIS - Lataa

TY - JOUR

T1 - Two Schemes of Privacy-Preserving Trust Evaluation

AU - Yan, Zheng

AU - Ding, Wenxiu

AU - Niemi, Valtteri

AU - Vasilakos, Athanasios V.

PY - 2016

Y1 - 2016

N2 - Trust evaluation computes trust values by collecting and processing trust evidence. It plays an important role in trust management that automatically ensures trust relationships among system entities and enhances system security. But trust evidence collection and process may cause privacy leakage, which makes involved entities reluctant to provide personal evidence that is essential for trust evaluation. Current literature pays little attention to Privacy-Preserving Trust Evaluation (PPTE). Existing work still has many limitations, especially on generality, efficiency and reliability. In this paper, we propose two practical schemes to guard privacy of trust evidence providers based on additive homomorphic encryption in order to support a traditional class of trust evaluation that contains evidence summation. The first scheme achieves better computational efficiency, while the second one provides greater security at the expense of a higher computational cost. Accordingly, two trust evaluation algorithms are further proposed to flexibly support different application cases. Specifically, these algorithms can overcome attacks raised by internal malicious evidence providers to some extent even though the trust evaluation is partially performed in an encrypted form. Extensive analysis and performance evaluation show the security and effectivity of our schemes for potential application prospect and their efficiency to support big data process.

AB - Trust evaluation computes trust values by collecting and processing trust evidence. It plays an important role in trust management that automatically ensures trust relationships among system entities and enhances system security. But trust evidence collection and process may cause privacy leakage, which makes involved entities reluctant to provide personal evidence that is essential for trust evaluation. Current literature pays little attention to Privacy-Preserving Trust Evaluation (PPTE). Existing work still has many limitations, especially on generality, efficiency and reliability. In this paper, we propose two practical schemes to guard privacy of trust evidence providers based on additive homomorphic encryption in order to support a traditional class of trust evaluation that contains evidence summation. The first scheme achieves better computational efficiency, while the second one provides greater security at the expense of a higher computational cost. Accordingly, two trust evaluation algorithms are further proposed to flexibly support different application cases. Specifically, these algorithms can overcome attacks raised by internal malicious evidence providers to some extent even though the trust evaluation is partially performed in an encrypted form. Extensive analysis and performance evaluation show the security and effectivity of our schemes for potential application prospect and their efficiency to support big data process.

KW - Big data

KW - Homomorphic encryption

KW - Privacy preservation

KW - Secure multiparty computation

KW - Trust evaluation

UR - http://www.scopus.com/inward/record.url?scp=84949255807&partnerID=8YFLogxK

U2 - 10.1016/j.future.2015.11.006

DO - 10.1016/j.future.2015.11.006

M3 - Article

VL - 62

SP - 175

EP - 189

JO - Future Generation Computer Systems: the international journal of grid computing and escience

JF - Future Generation Computer Systems: the international journal of grid computing and escience

SN - 0167-739X

ER -

ID: 2038703