Output privacy in data mining

Ting Wang, Ling Liu

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Privacy has been identified as a vital requirement in designing and implementing data mining systems. In general, privacy preservation 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 article presents a systematic study on the problem of protecting output privacy in data mining, and particularly, stream mining: (i) we highlight the importance of this problem by showing that even sufficient protection of input privacy does not guarantee that of output privacy; (ii) we present a general inferencing and disclosure model that exploits the intrawindow and interwindow privacy breaches in stream mining output; (iii) we propose a light-weighted countermeasure that effectively eliminates these breaches without explicitly detecting them, while minimizing the loss of output accuracy; (iv) we further optimize the basic scheme by taking account of two types of semantic constraints, aiming at maximally preserving utility-related semantics while maintaining hard privacy guarantee; (v) finally, we conduct extensive experimental evaluation over both synthetic and real data to validate the efficacy of our approach.

Original languageEnglish (US)
Article number1
JournalACM Transactions on Database Systems
Volume36
Issue number1
DOIs
StatePublished - Mar 1 2011

All Science Journal Classification (ASJC) codes

  • Information Systems

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