Chris Clifton

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Privacy-preserving distributed mining of association rules on horizontally partitioned data

Murat Kantarcioglu, Chris Clifton
IEEE Transactions on Knowledge and Data…
2002

Data mining can extract important knowledge from large data collections ut sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing… (More)

Privacy preserving association rule mining in vertically partitioned data

Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some… (More)

Tools for Privacy Preserving Distributed Data Mining

Chris Clifton, Murat Kantarcioglu, Jaideep Vaidya, Xiaodong Lin, Michael Y. Zhu
2003

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… (More)

Privacy-preserving k-means clustering over vertically partitioned data

Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid… (More)

SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

Wen-Syan Li, Chris Clifton
Data Knowl. Eng.
2000

One step in interoperating among heterogeneous databases is semantic integration: Identifying relationships between attributes or classes in dierent database schemas. SEMantic INTegrator (SEMINT) is… (More)

Secure set intersection cardinality with application to association rule mining

Jaideep Vaidya, Chris Clifton
Journal of Computer Security
2005

There has been concern over the apparent conflict between privacy and data mining. There is no inherent conflict, as most types of data mining produce summary results that do not reveal information… (More)

Privacy Preserving Data Mining

Jaideep Vaidya, Yu Zhu, Chris Clifton
Advances in Information Security
2006

A secure distributed framework for achieving k-anonymity

Wei Jiang, Chris Clifton
The VLDB Journal
2006

k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model… (More)

Hiding the presence of individuals from shared databases

Mehmet Ercan Nergiz, Maurizio Atzori, Chris Clifton
SIGMOD Conference
2007

Advances in information technology, and its use in research, are increasing both the need for anonymized data and the risks of poor anonymization. We present a metric, δ-presence, that clearly links… (More)

Using Unknowns to Prevent Discovery of Association Rules

Yücel Saygin, Vassilios S. Verykios, Chris Clifton
SIGMOD Record
2001

Data mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. We… (More)

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