Title :
Privacy-preserving data mining demonstrator
Author :
Beck, Martin ; Marhöfer, Michael
Author_Institution :
Tech. Univ. Dresden, Dresden, Germany
Abstract :
We present a system for demonstrating anonymized data mining on user profiles, which also evaluates the remaining utility of the generalized information through comparison of the classification results. The use case for this scenario builds around profiles containing personally identifiable information, which should be analyzed and classified by a third party. Our goal is to preserve the privacy of the users while maintaining a certain level of utility to allow data mining over the anonymized information.
Keywords :
data mining; data privacy; anonymized data mining; privacy-preserving data mining demonstrator; user privacy preservation; user profiles; Accuracy; Data models; Data privacy; Privacy; Runtime; Training; anonymization; data utility; demo; privacy preserving data mining;
Conference_Titel :
Intelligence in Next Generation Networks (ICIN), 2012 16th International Conference on
Conference_Location :
Berlin
Print_ISBN :
978-1-4673-1527-2
Electronic_ISBN :
978-1-4673-1525-8
DOI :
10.1109/ICIN.2012.6376028