A Collusion-Resistant Distributed Scalar Product Protocol With Application To Privacy-Preserving Computation of Trust

被引:8
|
作者
Melchor, Carlos Aguilar [1 ]
Ait-Salem, Boussad [1 ]
Gaborit, Philippe [1 ]
机构
[1] Univ Limoges, CNRS, UMR 6172, Xlim Lab, F-87000 Limoges, France
关键词
Privacy-preserving computation of trust; Secure multi-party computation; Secure scalar product; Superposed sending;
D O I
10.1109/NCA.2009.48
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Private scalar product protocols have proved to be interesting in various applications such as data mining, data integration, trust computing, etc. In 2007, Yao et al. proposed a distributed scalar product protocol with application to privacy-preserving computation of trust [1]. This protocol is split in two phases: an homorphic encryption computation; and a private multi-party summation protocol. The summation protocol has two drawbacks: first, it generates a non-negligible communication overhead; and second, it introduces a security flaw. The contribution of this present paper is two-fold. We first prove that the protocol of [1] is not secure in the semi-honest model by showing that it is not resistant to collusion attacks and we give an example of a collusion attack, with only four participants. Second, we propose to use a superposed sending round as an alternative to the multi-party summation protocol, which results in better security properties and in a reduction of the communication costs. In particular, regarding security, we show that the previous scheme was vulnerable to collusions of three users whereas in our proposal we can fix t is an element of [1..n - 1] and define a protocol resisting to collusions of up to t users.
引用
收藏
页码:140 / 147
页数:8
相关论文
共 50 条
  • [41] Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel
    Jung, Taeho
    Li, Xiang-Yang
    Wan, Meng
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2015, 12 (01) : 45 - 57
  • [42] Privacy-Preserving Trust-based Recommendations on Vertically Distributed Data
    Kaleli, Cihan
    Polat, Huseyin
    FIFTH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2011), 2011, : 376 - 379
  • [43] Privacy-Preserving Distributed Attribute Computation for Usage Control in the Internet of Things
    Costantino, Gianpiero
    La Marra, Antonio
    Martinelli, Fabio
    Mori, Paolo
    Saracino, Andrea
    2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE), 2018, : 1844 - 1851
  • [44] Web Application for Privacy-preserving Scheduling using Secure Computation
    Kiss, Agnes
    Schick, Oliver
    Schneider, Thomas
    PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS, VOL 2: SECRYPT, 2019, : 456 - 463
  • [45] Efficient privacy-preserving dot-product computation for mobile big data
    Hu, Chunqiang
    Huo, Yan
    IET COMMUNICATIONS, 2017, 11 (05) : 704 - 712
  • [46] Efficient and privacy-preserving outsourced unbounded inner product computation in cloud computing
    Yan, Jiayun
    Chen, Jie
    Qian, Chen
    Fu, Anmin
    Qian, Haifeng
    JOURNAL OF SYSTEMS ARCHITECTURE, 2024, 153
  • [47] A privacy-preserving mobile application recommender system based on trust evaluation
    Xu, Kun
    Zhang, Weidong
    Yan, Zheng
    JOURNAL OF COMPUTATIONAL SCIENCE, 2018, 26 : 87 - 107
  • [48] Application Analysis and Prospect of Privacy-preserving Computation in New Power System
    Fan H.
    Xu W.
    Fan X.
    Wang Y.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2023, 47 (19): : 187 - 199
  • [49] Scalable Privacy-Preserving t-Repetition Protocol with Distributed Medical Data
    Chun, Ji Young
    Hong, Dowon
    Lee, Dong Hoon
    Jeong, Ik Rae
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2012, E95A (12) : 2451 - 2460
  • [50] Fusion: Privacy-preserving Distributed Protocol for High-Dimensional Data Mashup
    Dagher, Gaby G.
    Iqbal, Farkhund
    Arafati, Mahtab
    Fung, Benjamin C. M.
    2015 IEEE 21ST INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2015, : 760 - 769