Privacy Preserving Aggregate Query of OLAP for Accurate Answers

Abstract

<p class="MsoNormal" style="margin: 0cm 0cm 0pt; mso-mirror-indents: yes;">In recent years, privacy protection has become an important topic when cooperative computation is performed in distributed environments. This paper puts forward efficient protocols for computing the multi-dimensional aggregates in distributed environments while keeping privacy preserving. We propose a novel model, which contains two crucial stages: local computation and cooperative computation based on secure multiparty computation protocols for privacy-preserving on-line analytical processing. According to the new model, we develop approaches to privacy-preserving <em style="mso-bidi-font-style: normal;">count</em> aggregate query over both horizontally partitioned data and vertically partitioned data. We, meanwhile, propose an efficient sub-protocol Two-Round Secure Sum Protocol. Theoretical analysis indicates that our solutions are secure and the answers are exactly accurate, that is, they can securely obtain the exact answer to aggregate query without revealing anything about their confidential data to each other. We also analyze detailedly the communication cost and computation complexity of our schemes in the paper and it shows that the new solutions have good linear complexity. No privacy loss and exact accuracy are two main significant advantages of our new schemes.</p&gt

A Secure Scalar Product Protocol and Its Applications to Computational Geometry

“…If the elements of vectors are negative, we can transform negative elements to positive elements, then perform scalar product of positive elements. For example, we would like to compute the scalar product of two vectors ( 2,3,6,7) − − and (4, 5,2, 6) − − in 15 Z , we can firstly transform them to (13,3,9,7) and (4,10,2,9) , then compute (…”

“…In secure multiparty computation (SMC) [1][2], a set of parties want to jointly compute a function of their inputs over the Internet or any computer network without revealing to the other participants any information about their private inputs. A secure scalar product protocol [3][4][5][6][7][8] is a type of specific SMC problem, and its goal is that two parties jointly compute the scalar product of their private vectors, but no party will reveal any information about his private vector to another party. As a building block, secure scalar product protocol has found various applications in many areas such as privacy-preserving data mining [3][4], privacy-preserving cooperative statistical analysis [5], and privacy-preserving geometry computation [6].…”

“…A secure scalar product protocol [3][4][5][6][7][8] is a type of specific SMC problem, and its goal is that two parties jointly compute the scalar product of their private vectors, but no party will reveal any information about his private vector to another party. As a building block, secure scalar product protocol has found various applications in many areas such as privacy-preserving data mining [3][4], privacy-preserving cooperative statistical analysis [5], and privacy-preserving geometry computation [6]. To solve this problem, many solutions have been proposed [3][4][5][6][7][8], and a nice overview of the problem and the security properties of some solutions were given in [3].…”

“…As a building block, secure scalar product protocol has found various applications in many areas such as privacy-preserving data mining [3][4], privacy-preserving cooperative statistical analysis [5], and privacy-preserving geometry computation [6]. To solve this problem, many solutions have been proposed [3][4][5][6][7][8], and a nice overview of the problem and the security properties of some solutions were given in [3].…”