Nowadays, information sharing as an indispensable part appears in our vision, bringing about a mass of discussions about methods and techniques of privacy preserving data publishing which are regarded as strong guarantee to avoid information disclosure and protect individuals’ privacy. Recent work focuses on proposing different anonymity algorithms for varying data publishing scenarios to satis...

Nowadays, publishing data publically is an important for many purposes especially for scientific research. Publishing this data in its raw form make it vulnerable to privacy attacks. Therefore, there is a need to apply suitable privacy preserving techniques on the published data. K-anonymity and L-diversity are well known techniques for data privacy preserving. These techniques cannot face the ...

Recent work on anonymizing online social networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. We study the problem of repeatedly publishing OSN data as the network evolves while preserving privacy of users. Publi...

Privacy-preserving data mining techniques could encourage health data custodians to provide accurate information for mining by ensuring that the data mining procedures and results cannot, with any reasonable degree of certainty, violate data privacy. We outline privacypreserving data mining techniques/systems in the literature and in industry. They range from privacy-preserving data publishing,...

Publishing the data with multiple sensitive attributes brings us greater challenge than publishing the data with single sensitive attribute in the area of privacy preserving. In this study, we propose a novel privacy preserving model based on k-anonymity called (α, β, k)-anonymity for databases. (α, β, k)anonymity can be used to protect data with multiple sensitive attributes in data publishing...

Recently, privacy preserving data publishing has attracted significant interest in research. Most of the existing studies focus on only the situations where the data in question is published using one quasi-identifier. However, in a few important applications, a practical demand is to publish a data set on multiple quasi-identifiers for multiple users simultaneously, which poses several challen...