C
Chris Clifton
Researcher at Purdue University
Publications - 160
Citations - 12051
Chris Clifton is an academic researcher from Purdue University. The author has contributed to research in topics: Information privacy & Privacy software. The author has an hindex of 54, co-authored 160 publications receiving 11501 citations. Previous affiliations of Chris Clifton include Princeton University & Qatar University.
Papers
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Journal ArticleDOI
Privacy-preserving distributed mining of association rules on horizontally partitioned data
Murat Kantarcioglu,Chris Clifton +1 more
TL;DR: In this paper, the authors address secure mining of association rules over horizontally partitioned data. And they incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
Journal ArticleDOI
Tools for privacy preserving distributed data mining
TL;DR: This paper presents some components of a toolkit of components that can be combined for specific privacy-preserving data mining applications, and shows how they can be used to solve several Privacy preserving data mining problems.
Proceedings ArticleDOI
Privacy preserving association rule mining in vertically partitioned data
Jaideep Vaidya,Chris Clifton +1 more
TL;DR: In this paper, the authors present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values, but the authors do not consider the privacy concerns of individual transaction data.
Proceedings ArticleDOI
Privacy-preserving k-means clustering over vertically partitioned data
Jaideep Vaidya,Chris Clifton +1 more
TL;DR: This work presents a method for k-means clustering when different sites contain different attributes for a common set of entities, where each site learns the cluster of each entity, but learns nothing about the attributes at other sites.
Journal ArticleDOI
SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks
Wen-Syan Li,Chris Clifton +1 more
TL;DR: Theoretical background and implementation details of SEMINT are provided and experimental results from large and complex real databases are presented.