In privacy-preserving data mining, there is a need to consider on-line data collection applications in a client-server-to-user (CS2U) model, in which a trusted server can help clients create and disseminate anonymous data. Existing privacy-preserving data publishing (PPDP) and privacy-preserving data collection (PPDC) methods do not sufficiently address the needs of these applications. In this ...

Recent advances in processing and storing information has led to an explosion of data collec-tion. Many organizations like the Census, hospitals and even search engine companies collect, analyze anddistribute personal information in return for useful services. However, the collected data track entire pub-lic and private lives of individuals, thus resulting in an immense privacy risk...

In this paper we propose and prove a new technique called “Overlapping Slicing” for privacy preservation of high dimensional data. The process of publishing the data in the web, faces many challenges today. The data usually contains the personal information which are personally identifiable to anyone, thus poses the problem of Privacy. Privacy is an important issue in data publishing. Many orga...

Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. However, many solutions exist for privacy preserving data; differential privacy has emerged as a new paradigm for privac...

The release of detailed taxi trips has motivated numerous useful studies, but has also triggered multiple privacy attacks on individuals’ trips. Despite these attacks, no tools are available for systematically analyzing the privacy risk of released trajectory data. While, recent studies have proposed mechanisms to publish synthetic mobility data with provable privacy guarantees, the questions o...

Privacy-preserving microdata publishing currently lacks a solid theoretical foundation. Most existing techniques are developed to satisfy syntactic privacy notions such as k-anonymity, which fails to provide strong privacy guarantees. The recently proposed notion of differential privacy has been widely accepted as a sound privacy foundation for statistical query answering. However, no general p...

Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring utility remains an important goal to achieve. Because finding the trade-off between privacy and is NP-hard problem also current research area. When existing approaches are investigated, one of most significant difficulties discovered presence outlier in datasets. ...

OBJECTIVE Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, ε-differential privacy provides one of the strongest privacy guarantees and makes no assumptions about an adversary's background knowledge. All existing solutions that ensure ε-differential privacy handle the problem of disclosing rel...