01411nas a2200157   4500008003900000245007400039210006900113300001400182520091000196100001701106700001901123700002001142700002601162700002101188856004401209       2012                          d00aInjecting Discrimination and Privacy Awareness Into Pattern Discovery0 aInjecting Discrimination and Privacy Awareness Into Pattern Disc  a360–3693 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.1 aHajian, Sara1 aMonreale, Anna1 aPedreschi, Dino1 aDomingo-Ferrer, Josep1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/ICDMW.2012.51