Abstract Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It used in many areas ranging from business analysis to health care. In of these applications, sensitive information clustered should not be leaked. Moreover, nowadays it often required combine data multiple sources increase the quality as well outsource complex computation...

Abstract Loyalty programs in the form of punch cards that can be redeemed for benefits have long been a ubiquitous element consumer landscape. However, their increasingly popular digital equivalents, while providing more convenience and better bookkeeping, pose considerable privacy risk. This paper introduces privacy-preserving card protocol allows firms to digitize loyalty without forcing cust...

As a powerful tool in solving privacy preserving cooperative problems, secure multiparty computation is more and more popular in electronic bidding, anonymous voting, and online auction. Privacy preserving sequencing problem which is an essential link is regarded as the core issue in these applications. However, due to the difficulties of solvingmultiparty privacy preserving sequencing problem,...

Actually, a lot of attention focusing on the problem of computing privacy-preserving OLAP cubes effectively and efficiently arises. State-of-theart proposals rather focus on an algorithmic vision of the problem, and neglect relevant theoretical aspects the investigated problem introduces naturally. In order to fulfill this gap, in this paper we provide algorithms for supporting privacy-preservi...

Many wireless sensor network (WSN) applications require privacy-preserving aggregation of sensor data during transmission from the source nodes to the sink node. In this paper, we explore several existing privacy-preserving data aggregation (PPDA) protocols for WSNs in order to provide some insights on their current status. For this, we evaluate the PPDA protocols on the basis of such metrics a...