Multi level privacy preserving in medical data publishing

Neera K. Sali, Diya Thomas

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Privacy preserving data publishing (PPDF) is an emerging technology in the datamining field which performs datamining operations in a secured manner to preserve the confidential/sensitive information. Preserving privacy while publishing medical information has become an important challenge in this area due to its high confidentiality. While publishing medical data the PPDF scheme should maximize the data utility at the same time should have a minimum data disclosure risk. This paper concerned with privacy of medical data while publishing the patient information for research or analysis purposes. K-anonymity and l-diversity are the most popular techniques used for preserving privacy. These techniques does not consider the semantic relationship between the data values so they are prone to similarity attack .In this paper, we present a privacy-preserving data publishing framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privileges. The proposed system overcome the similarity attack by applying a privacy preservation approach which uses a key attribute masking technique and an anonymization process.The results showed that the semantic anonymization increases the privacy level with effective data utility.

Original languageEnglish
Title of host publicationCreative trends in engineering and technology
EditorsAarti Singh, Ferras Hanandeh
Place of PublicationNew Delhi
PublisherNarosa Publishing House
Number of pages6
Publication statusPublished - 2016
Externally publishedYes
Event7th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2016 - Hyderabad, India
Duration: 12 Aug 201613 Aug 2016


Conference7th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2016


  • Categorical data
  • K-anonymity
  • L-diversity
  • Privacy preserving data publishing


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