Chris Clifton

About

Chris Clifton is a Professor of Statistics at Purdue University. His research interests include Data Mining, Data Science, Machine Learning, and Statistical Disclosure Limitation. He holds a B.S. in Computer Science and Engineering from the Massachusetts Institute of Technology, earned in 1986, and an M.S. in Electrical Engineering and Computer Science from MIT, also in 1986. He completed his Ph.D. in Computer Science at Princeton University in 1991. Clifton has been recognized with numerous awards, including being named an IEEE Fellow in 2020 and an ACM Distinguished Member in 2017. His contributions to the field have been acknowledged through awards such as the IEEE International Conference on Data Mining Outstanding Service Award in 2011, and the College of Science Graduate Student Mentoring Award in 2009. He is actively involved in research and service within the Department of Statistics at Purdue University.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Data Mining
• Statistics
• Algorithm
• Engineering
• Mathematics

Selected publications

• A governance framework for algorithmic accountability and transparency

  Repository@Nottingham (University of Nottingham) · 2019-04-04 · 105 citations

  articleOpen access

  Algorithmic systems are increasingly being used as part of decision-making processes in both the public and private sectors, with potentially significant consequences for individuals, organisations and societies as a whole. Algorithmic systems in this context refer to the combination of algorithms, data and the interface process that together determine the outcomes that affect end users. Many types of decisions can be made faster and more efficiently using algorithms. A significant factor in the…

  PublisherOA PDFDOI

• Differentially Private Naïve Bayes Classifier Using Smooth Sensitivity

  Proceedings on Privacy Enhancing Technologies · 2021 · 11 citations

  Senior authorCorresponding

  There is increasing awareness of the need to protect individual privacy in the training data used to develop machine learning models. Differential Privacy is a strong concept of protecting individuals. Naïve Bayes is a popular machine learning algorithm, used as a baseline for many tasks. In this work, we have provided a differentially private Naïve Bayes classifier that adds noise proportional to the smooth sensitivity of its parameters. We compare our results to Vaidya, Shafiq, Basu, and Hong…

  DOI

• Differentially Private Feature Selection for Data Mining

  2018-03-15 · 9 citations

  articleSenior author

  One approach to analysis of private data is ε-differential privacy, a randomization-based approach that protects individual data items by injecting carefully limited noise into results. A challenge in applying this to private data analysis is that the noise added to the feature parameters is directly proportional to the number of parameters learned. While careful feature selection would alleviate this problem, the process of feature selection itself can reveal private information, requiring the…

  PublisherDOI

• On Improving Fairness of AI Models with Synthetic Minority Oversampling Techniques

  Society for Industrial and Applied Mathematics eBooks · 2023 · 8 citations

  Senior authorCorresponding

  Biased AI models result in unfair decisions. In response, a number of algorithmic solutions have been engineered to mitigate bias, among which the Synthetic Minority Oversampling Technique (SMOTE) has been studied, to an extent. Although the SMOTE technique and its variants have great potentials to help improve fairness, there is little theoretical justification for its success. In addition, formal error and fairness bounds are not clearly given. This paper attempts to address both issues. We pr…

  PublisherDOI

• Differentially Private <i>k</i> -Nearest Neighbor Missing Data Imputation

  ACM Transactions on Privacy and Security · 2022-03-29 · 8 citations

  article1st authorCorresponding

  Using techniques employing smooth sensitivity , we develop a method for \( k \) -nearest neighbor missing data imputation with differential privacy. This requires bounding the number of data incomplete tuples that can have their data complete “donor” changed by making a single addition or deletion to the dataset. The multiplicity of a single individual’s impact on an imputed dataset necessarily means our mechanisms require the addition of more noise than mechanisms that ignore missing data, but…

  PublisherDOI

Recent grants

• ITR - (ASE+NHS) - (dmc+int): Privacy-Preserving Data Integration and Sharing

  NSF · $1.0M · 2004–2008

• Shared Software Database

  NSF · $90k · 1992–1995

Frequent coauthors

• Murat Kantarcıoğlu

  The University of Texas at Dallas

  24 shared

• Jaideep Vaidya

  24 shared

• Wen‐Syan Li

  Seoul National University

  14 shared

• Koray Mancuhan

  10 shared

• Mehmet Ercan Nergiz

  Teknoloji Arastirma ve Gelistirme Endustriyel Urunler Bilisim Teknolojileri San Tic

  9 shared

• Wei Jiang

  Soochow University

  8 shared

• Bhavani Thuraisingham

  8 shared

• M. Murugesan

  Sri Sivasubramaniya Nadar College of Engineering

  7 shared

Education

• B.S., Computer Science and Engineering

  Massachusetts Institute of Technology

  1986

• M.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  1986

• M.A., Computer Science

  Princeton University

  1988

• Ph.D., Computer Science

  Princeton University

  1991

Awards & honors

• IEEE Fellow (2020)
• ACM Distinguished Member (2017)
• College of Science Team Award (2011)
• IEEE International Conference on Data Mining Outstanding Ser…
• Teaching for Tomorrow Award (2011)

Similar researchers at Purdue University

• William S. Clevelandh-53
• Bruce A. Craigh-47
• Anirban DasGuptah-44