Privacy Preserving Association Rule in Data Mining

Privacy is an important issue in Data mining. The privacy field has seen speedy advances in current years because ability to store data has increased. Precisely, current advances in the data mining field have led to increased concerns about privacy. Privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. . Many methods have been brought out to solve this. As a result privacy becomes one of the prime anxieties in data mining research public. A new class of data mining approaches, known as privacy preserving data mining algorithms, has been developed by the research public working on security and knowledge discovery. The goal of these algorithms is the extraction of relevant knowledge from large amount of digital data and while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been advanced that allow one to hide sensitive item sets or patterns, before the data mining process is executed. Association rule mining helps to preserves the confidentiality of each database. To find the association rule, each participant has to segment their own data. Thus, much privacy information may be transmitted or been illegal used. Association rule mining is one of the vital problems in data mining, privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Keywords—Data mining, Data hiding, Knowledge hiding, Association Rule, Privacy preserving