Output Privacy in Data Mining

Privacy has been identified as a vital requirement in designing and implementing data mining systems. In general, privacy preservation in data mining demands protecting both input and output privacy: the former refers to sanitizing the raw data itself before performing mining; while the latter refers to preventing the mining output (Models or patterns) from malicious inference attacks. This paper presents a systematic study on the problem of protecting output privacy in data mining, and particularly, stream mining: this paper highlights the importance of this problem by showing that even sufficient protection of input privacy does not guarantee that of output privacy; this paper presents a general inferencing and disclosure model that exploits the intra-window and inter-window privacy breaches in stream mining output.

Provided by: Georgia Institute of Technology Topic: Big Data Date Added: Aug 2010 Format: PDF

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