Privacy Preserving Association Rule Mining Using Perturbation Technique

The information age has enabled organizations to gather large volumes of data. However, the usefulness of this data is negligible if “meaningful information” cannot be extracted from it. Data mining answers this need. The problem of privacy-preserving data mining has become important recently because of the increasing ability to store personal data and the sophistication of data mining algorithms to leverage information. Many researches have been done in this field but few with quantitative data had drawbacks of high number of rules generated and few number of item hidden. This study, proposes a perturbation association rules hiding algorithm for privacy of quantitative data to provide a better algorithm for preserving quantitative data. In hiding of rules, the noise associated with each item was calculated. The noise was used to calculate the support and confidence of rules which were then compared with minimum support and confidence. Item whose support/confidence is less than or equal to minimum support or confidence would be hidden. Experimental result shows that the algorithm hides more rules than the existing works. CCS Concepts • Information systems ➝ Information systems applications ➝ Data mining ➝ Association rules • Security and privacy ➝ Human and societal aspects of security and privacy ➝ Privacy protection