Abstract
The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data mining problems. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSIDENT is proposed. The results of the work have been given.
Highlights
In recent years, data mining or knowledge discovery in databases has developed into an important technology of identifying patterns and trends from large quantities of data
The problem of mining association rules is to find all rules that are greater than the user-specified minimum support and minimum confidence
The objective of privacy preserving data mining is to hide certain sensitive information so that they cannot be discovered through data mining techniques (Oliveira, Zaiane, 2003 a, Oliveira, Zaiane, 2003 b)
Summary
Data mining or knowledge discovery in databases has developed into an important technology of identifying patterns and trends from large quantities of data. Suppose we (as purchasing directors of BigMart, a large supermarket chain) are negotiating a deal with the Dedtrees paper company They offer to us a reduced price if we agree to give them access to our database of customer purchases. When we go to negotiate with Dedtrees, we find that with reduced competition, they are unwilling to offer us as low a price, and we start to lose business to our competitors This example indicates the need to prevent disclosure of confidential personal information from summarized or aggregated data, and to prevent data mining techniques from discovering sensitive knowledge which is not even known to the database owners.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
