K
Krishnan Pillaipakkamnatt
Researcher at Hofstra University
Publications - 16
Citations - 732
Krishnan Pillaipakkamnatt is an academic researcher from Hofstra University. The author has contributed to research in topics: Disjunctive normal form & Differential privacy. The author has an hindex of 11, co-authored 16 publications receiving 684 citations. Previous affiliations of Krishnan Pillaipakkamnatt include Vanderbilt University.
Papers
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Journal ArticleDOI
A Practical Differentially Private Random Decision Tree Classifier
TL;DR: In this paper, the problem of constructing private classifiers using decision trees, within the framework of differential privacy, was studied and a differentially private decision tree ensemble algorithm based on random decision trees was proposed.
Proceedings ArticleDOI
A Practical Differentially Private Random Decision Tree Classifier
TL;DR: This paper first constructs privacy-preserving ID3 decision trees using differentially private sum queries, then presents a differentiallyPrivate decision tree ensemble algorithm using the random decision tree approach, which shows good prediction accuracy even when the size of the datasets is small.
Proceedings Article
A New Privacy-Preserving Distributed k-Clustering Algorithm
TL;DR: A simple I/O-efficient k-clustering algorithm that was designed with the goal of enabling a privacy-preserving version of the algorithm and produces cluster centers that are, on average, more accurate than the ones produced by the well known iterative k-means algorithm.
Journal ArticleDOI
How many queries are needed to learn
TL;DR: It is shown that an honest class is exactly polynomial-query learnable if and only if it is learnable using an oracle for Γ p 4, and a new relationship between query complexity and time complexity in exact learning is shown.
Proceedings ArticleDOI
How many queries are needed to learn
TL;DR: It is shown that an honest class is exactly polynomial-query learnable if and only if it is learnable using an oracle for Γp4, and a new relationship between query complexity and time complexity in exact learning is shown.