Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. To encourage users to...
Privacy concerns have become an important issue in data mining. A popular way to preserve privacy is to randomize the dataset to be mined in a systematic way and mine the randomiz...
Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privac...
This report summarizes the events of the 2nd International Workshop on Privacy, Security, and Trust in KDD, at the 14th ACM SIGKDD International Conference on Knowledge Discovery ...
Francesco Bonchi, Elena Ferrari, Wei Jiang, Bradle...
Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where...