8 citations · 23 across the 6 of their papers we have counts for
7 papers
A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report
Chris Clifton, Bradley Malin, Anna Oganian +2
Government agencies collect and manage a wide range of ever-growing datasets. While such data has the potential to support research and evidence-based policy making, there are conc…
Improving Fairness of AI Systems with Lossless De-biasing
Yan Zhou, Murat Kantarcioglu, Chris Clifton
In today's society, AI systems are increasingly used to make critical decisions such as credit scoring and patient triage. However, great convenience brought by AI systems comes wi…
Differentially Private Imaging via Latent Space Manipulation
Tao Li, Chris Clifton
There is growing concern about image privacy due to the popularity of social media and photo devices, along with increasing use of face recognition systems. However, established im…
Differentially Private Naive Bayes Classifier using Smooth Sensitivity
Farzad Zafarani, Chris Clifton
With the increasing collection of users' data, protecting individual privacy has gained more interest. Differential Privacy is a strong concept of protecting individuals. Naive Bay…
K-Nearest Neighbor Classification Using Anatomized Data
Koray Mancuhan, Chris Clifton
This paper analyzes k nearest neighbor classification with training data anonymized using anatomy. Anatomy preserves all data values, but introduces uncertainty in the mapping betw…
Statistical Learning Theory Approach for Data Classification with l-diversity
Koray Mancuhan, Chris Clifton
Corporations are retaining ever-larger corpuses of personal data; the frequency or breaches and corresponding privacy impact have been rising accordingly. One way to mitigate this…
Decision Tree Classification on Outsourced Data
Koray Mancuhan, Chris Clifton
This paper proposes a client-server decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the server sees data values, but th…