Privacy-Preserving Distributed Statistical Computation to a Semi-Honest Multi-Cloud

被引：0
|
作者：
Calvino, Aida [1 ]
Ricci, Sara [1 ]
Domingo-Ferrer, Josep [1 ]
机构：
[1] Univ Rovira & Virgili, Dept Comp Engn & Math, UNESCO Chair Data Privacy, Tarragona, Spain
来源：
2015 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS) | 2015年
关键词：
Privacy; data splitting; covariance matrix; cloud computing; scalar product;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present the problem of privacy-preserving distributed statistical computing (PPDSC) in which one party vertically splits a data set among a set of honest-butcurious clouds and wishes to use the clouds' processing power to perform statistical computation on the overall data set. The cornerstone is to compute covariances and, more specifically, scalar products. Existing protocols for computing scalar products on split data are identified and compared, and new variants specifically designed for PPDSC are presented that improve privacy and performance.
引用
收藏
页码：506 / 514
页数：9
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