Publishing set valued data via m-privacy
1m-Privacy for Collaborative Data Publishing - CORE Abstract. Abstract—In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of “insider attack ” by colluding data providers who may use their own data records (a subset of the overall data) to infer the data records contributed by CiteSeerX — Citation Query Computational differential Abstract - Cited by 139 (3 self) - privacy-preserving statistical analysis of data. The problem of statistical disclosure control – revealing accurate statistics about a set of respondents while preserving the privacy of individuals – has a venerable history, with an extensive literature spanning statistics, theoretical computer science RESEARCH ARTICLE A Novel Framework for Preventing
Disclosure control has become inevitable as privacy is given paramount importance while publishing data for mining. The data mining community enjoyed revival after Samarti and Sweeney proposed k-anonymization for privacy preserving data mining. The k-anonymity has gained high popularity in research circles. Though it has some drawbacks and other PPDM algorithms such as l-diversity, t-closeness
M privacy for collaborative data publishing The collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers … Enhanced M-Privacy for Collaborative Data Publishing The consumption of these protocols can make collaborative data publishing more effective and enhanced using m-privacy. ACKNOWLEDGMENTS Sincere thank to the reviewers for reviewing this manuscript and providing inputs for greatly improving the quality of this paper. m-Privacy for Collaborative Data Publishing
IJET - Vol3 Issue 5
In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may use their own data records (a subset of the overall data) in addition to the external background knowledge to infer the [PDF] Parallelizing K-Anonymity Algorithm for Privacy