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Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.
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Chris Clifton
Purdue University West Lafayette
Murat Kantarcıoğlu
The University of Texas at Dallas
Jaideep Vaidya
Rutgers, The State University of New Jersey
ACM SIGKDD Explorations Newsletter
Purdue University West Lafayette
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Clifton et al. (Sun,) studied this question.
synapsesocial.com/papers/6a105f7de1a472cb5efcca21 — DOI: https://doi.org/10.1145/772862.772867
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