While most anonymization technology available today is designed for static and small data, the current picture of massive volumes dynamic data arriving at unprecedented velocities. From standpoint anonymization, challenging type streams. However, while majority proposals deal with publishing either count-based or aggregated statistics about underlying stream, little attention has been paid to p...

Data sharing between different organizations is an essential process in today’s connected world. However, recently there were many concerns about data as sensitive information can jeopardize users’ privacy. To preserve privacy, use anonymization techniques to conceal data. these are vulnerable de-anonymization attacks which aim identify individual records within a dataset. In this paper, two-ti...

Big data security has grown in importance as a result of its strong correlation to users. Because big contains individually identifying information, it poses considerable safety issue. To protect personal confidentiality is commonly employed non-interactive information sharing and transmission settings. This entails hiding identifiable confidential material from the owners acquired data.Anonymi...

Social networks have received dramatic interest in research and development. In this chapter, we survey the very recent research development on privacy-preserving publishing of graphs and social network data. We categorize the state-of-the-art anonymization methods on simple graphs in three main categories: K-anonymity based privacy preservation via edge modification, probabilistic privacy pres...

This paper proposes a cell-suppression based k-anonymization method which keeps minimal the loss of utility. The proposed method uses the Kullback-Leibler (KL) divergence as a utility measure derived from the notions developed in the literature of incomplete data analysis, including the missing-at-random (MAR) condition. To be more specific, we plug the KL divergence into an bottom-up, greedy p...

Private data such as electronic health records and banking transactions must be shared within the cloud environment to analysis or mine data for research purposes. In big data applications, data privacy is one of the most concerned issue because processing large-scale privacy-sensitive data sets often requires computation power provided by public cloud services. Introducing a technique called D...