A Novel Framework for Privacy Conserving Data Publishing and Handling High Dimensional Data



Now a day’s Data publishing process is the compulsory for visualizing the data sets to other parties. But in publishing process there is thread for data set owners by disclosing the sensitive information. For avoiding those problems we using anonymization techniques for secure data publishing. Most Anonymization techniques such as Generalization and Bucketization are using now. But these anonymization techniques have some limitation. Generalization for k-anonymity losses considerable amount of information for high- dimensional data and Bucketization does not prevent membership disclosure, because bucketization publishes the QI values in their original forms, It requires a clear separation between QI’s and SA’s, but in many data sets, it is unclear which attributes are QI’s and which are SA’s and it breaks the attributes correlations between the QI’s and the SA’s. This paper introduces new anonymization technique slicing to overcome all the drawbacks of bucketization and generalization


Keywords: - Sensitive information, High dimensional data, data anonymization, data publishing, data security, generalization and bucketization.

Full Text:


DOI: https://doi.org/10.26483/ijarcs.v5i2.2025


  • There are currently no refbacks.

Copyright (c) 2016 International Journal of Advanced Research in Computer Science