High quality and useful knowledge is to be found in the integrated data from various organizations, and the discovered knowledge is essential for building intelligent systems such as business analysis and health surveillance. However, concern about breaching privacy is a major obstacle of this process. This project aims to develop new efficient and effective techniques for privacy protection in...

Privacy preserving data publishing (PPDP) methods a new class of privacy preserving data mining (PPDM) technology, has been developed by the research community working on security and knowledge discovery. It is common to share data between two organizations in many application areas. When data are to be shared between parties, there could be some sensitive patterns which should not be disclosed...

Privacy preserving is one of the most important research topics in the data security field and it has become a serious concern in the secure transformation of personal data in recent years. For example, different credit card companies and disease control centers may try to build better data sharing or publishing models for privacy protection through privacy preserving data mining techniques (PP...

In this paper we illustrate a privacy framework named Indistinguishable Privacy. Indistinguishable privacy could be deemed as the formalization of the existing privacy definitions in privacy preserving data publishing as well as secure multi-party computation. We introduce three representative privacy notions in the literature, Bayes-optimal privacy for privacy preserving data publishing, diffe...

Privacy preserving publishing is the kind of techniques to apply privacy to collected vast amount of data. One of the recent problem prevailing is in the field of data publication. The data often consist of personally identifiable information so releasing such data consists of privacy problem. Several anonymization techniques such as generalization and bucketization have been designed for priva...