World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
15621
World Ranking
3399
National Ranking
1649

Research.com Recognitions

  • 2017 - ACM Distinguished Member
  • 2006 - ACM Senior Member

Overview

Chris Clifton is affiliated with Purdue University West Lafayette in the United States. Their research spans multiple disciplines, with a primary focus on Computer Science, Engineering, and Social Sciences. Their work integrates subfields such as Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science, Computer Vision and Pattern Recognition, and Safety Research.

The scientist's research topics cover a range of areas including Privacy-Preserving Technologies in Data, Privacy, Security, and Data Protection, Millimeter-Wave Propagation and Modeling, Microwave Engineering and Waveguides, Ethics and Social Impacts of AI, Cryptography and Data Security, and Advanced MIMO Systems Optimization.

Recent research papers authored or co-authored by Chris Clifton include:

  • Differentially Private Naïve Bayes Classifier Using Smooth Sensitivity (2021), published in DOAJ (DOAJ: Directory of Open Access Journals)
  • Differentially Private Imaging via Latent Space Manipulation (2021), published in arXiv (Cornell University)
  • Differentially Private k -Nearest Neighbor Missing Data Imputation (2022), published in ACM Transactions on Privacy and Security
  • Improving Fairness of AI Systems with Lossless De-biasing (2021), published in arXiv (Cornell University)
  • Unfair AI: It Isn't Just Biased Data (2022), published in 2022 IEEE International Conference on Data Mining (ICDM)

Their frequent co-authors include MinKeun Chung, Liang Liu, Andréas Johansson, Zhinong Ying, and Olof Zander.

Publication venues where Chris Clifton's work frequently appears include:

  • arXiv (Cornell University)
  • DOAJ (DOAJ: Directory of Open Access Journals)
  • ACM Transactions on Privacy and Security
  • 2022 IEEE International Conference on Data Mining (ICDM)
  • IEEE Transactions on Wireless Communications

Throughout their career, Chris Clifton has received professional recognition including being named an ACM Distinguished Member in 2017 and an ACM Senior Member in 2006.

Best Publications

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

    M. Kantarcioglu;C. Clifton

  • Privacy preserving association rule mining in vertically partitioned data

    Jaideep Vaidya;Chris Clifton

  • Tools for privacy preserving distributed data mining

    Chris Clifton;Murat Kantarcioglu;Jaideep Vaidya;Xiaodong Lin

  • Privacy-preserving k-means clustering over vertically partitioned data

    Jaideep Vaidya;Chris Clifton

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

    Wen-Syan Li;Chris Clifton

  • Using unknowns to prevent discovery of association rules

    Yücel Saygin;Vassilios S. Verykios;Chris Clifton

  • Hiding the presence of individuals from shared databases

    Mehmet Ercan Nergiz;Maurizio Atzori;Chris Clifton

  • SECURITY AND PRIVACY IMPLICATIONS OF DATA MINING

    C Clifton;D Marks

  • Semantic Integration in Heterogeneous Databases Using Neural Networks

    Wen-Syan Li;Chris Clifton

  • On Syntactic Anonymity and Differential Privacy

    Chris Clifton;Tamir Tassa

  • Multirelational k-Anonymity

    M.E. Nergiz;C. Clifton;A.E. Nergiz

  • Secure set intersection cardinality with application to association rule mining

    Jaideep Vaidya;Chris Clifton

  • Privacy-preserving Naïve Bayes classification

    Jaideep Vaidya;Murat Kantarcıoğlu;Chris Clifton

  • Privacy-preserving data integration and sharing

    Chris Clifton;Murat Kantarcioǧlu;AnHai Doan;Gunther Schadow

  • A secure distributed framework for achieving k -anonymity

    Wei Jiang;Chris Clifton

  • Query flocks: a generalization of association-rule mining

    Dick Tsur;Jeffrey D. Ullman;Serge Abiteboul;Chris Clifton

  • Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data.

    Jaideep Vaidya;Chris Clifton

  • Defining Privacy for Data Mining

    Chris Clifton;Murat Kantarcioglu;Jaideep Vaidya

  • Thoughts on k-anonymization

    M. Ercan Nergiz;Chris Clifton

  • How much is enough? choosing ε for differential privacy

    Jaewoo Lee;Chris Clifton

  • Privacy-preserving decision trees over vertically partitioned data

    Jaideep Vaidya;Chris Clifton;Murat Kantarcioglu;A. Scott Patterson

  • Proceedings of the 2011 SIAM International Conference on Data Mining

    Bing Liu;Huan Liu;Chris Clifton;Takashi Washio

Frequent Co-Authors

Jaideep Vaidya
Jaideep Vaidya Rutgers, The State University of New Jersey
Murat Kantarcioglu
Murat Kantarcioglu The University of Texas at Dallas
Bhavani Thuraisingham
Bhavani Thuraisingham The University of Texas at Dallas
Luo Si
Luo Si Alibaba Group (China)
Hector Garcia-Molina
Hector Garcia-Molina Stanford University
Elisa Bertino
Elisa Bertino Purdue University West Lafayette
Raghu Ramakrishnan
Raghu Ramakrishnan Microsoft (United States)
Elena Ferrari
Elena Ferrari University of Insubria
Bing Liu
Bing Liu University of Illinois at Chicago
Heikki Mannila
Heikki Mannila Aalto University

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