"Sensitive Proprietary Patterns Discovered in Data Mining Given Privacy Boost"

Researchers at Chongqing University have boosted the privacy and protection of proprietary or other sensitive information during data mining without compromising the ability to discover useful patterns in huge datasets. Data mining is the discovery of patterns in enormous sets of data and the sharing of that information for useful purposes, which often involves Machine Learning (ML). Oftentimes, data mining hits a roadblock when such data patterns are proprietary, undermine privacy, or compromise security. However, such data sharing or publication pushes further discovery of useful patterns that benefit the owners of those datasets and society at large. This article continues to discuss the importance of data mining and the technique developed by the researchers to support association rule mining on published datasets while providing privacy for certain rules. 

SCIENMAG reports "Sensitive Proprietary Patterns Discovered in Data Mining Given Privacy Boost"