Currently, the computational power present in sensors forming a wireless sensor network (WSN) allows for implementing most of data processing and analysis directly on decentralized way. This shift paradigm introduces privacy security problems that need to be addressed. While implementation avoids single point failure problem typically applies centralized approaches, it is subject other threats,...

The development in data mining technology brings serious threat to the individualinformation. The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data. The unwanted disclosure of the sensitive information may happen during the process of data mining results. In this paper we identify four different types of users involved in mining a...

As a new computing model, Granular computing provides a new efficient way for solving complicated problems, massive data mining, and fuzzy information processing. Privacy is becoming an increasingly important issue in many data mining applications. In this paper, we combined the existing model of granular computing with personalized privacy-preserving demand, and proposed a new granular computi...

Data mining is a key technology in big data analytics and it can discover understandable knowledge (patterns) hidden in large data sets. Association rule is one of the most useful knowledge patterns, and a large number of algorithms have been developed in the data mining literature to generate association rules corresponding to different problems and situations. Privacy becomes a vital issue wh...

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional ...

Data mining often causes privacy concerns. To ease the concerns, various privacy preserving data mining techniques have been proposed. However, those techniques are often too computationally intensive to be deployed in practice. Efficiency becomes a major challenge in privacy preserving data mining. In this paper we present an efficient secure dot product protocol and show its application in pr...

Data mining has emerged as a significant technology for gaining knowledge from vast quantities of business data, financial data, networked data and medical data. The goal of data mining is approaches are to develop generalized knowledge rather than identify specific information against specific individual. There has been growing concern that use of this technology is violating individual privac...

In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. We also present comm...