Privacy Preservation in Quantitative Association Rule Mining Using Fuzzy Logic-Based Sanitization

In the present world order, where the use of artificial intelligence and machine learning has been predominant in various industries, information and data are of prime importance. Various algorithms have made it possible to analyze large databases and provide insights for making strategic decisions. One such tool is data mining, where relations between items in transactional databases can be retrieved. Such relations can have great value in the proper situation, providing immense value to the observer. In contrast, some associations can also be deemed sensitive by the data publishers and hence, have to be sanitized before being made public. This is where PPDM (Privacy Preserving Data Mining) comes into play. The below research presents a way to sanitize quantitative databases based on fuzzy logic.