Krishnan Pillaipakkamnatt
Professor of Computer Science Hofstra University
- Hempstead NY
Krishnan Pillaipakkamnatt specializes in data mining, machine learning, and computational learning theory.
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Biography
Professor Pillaipakkamnatt's research interests lie in data mining, and machine learning. More narrowly, he is interested in algorithms that preserve an individual's privacy. A second area of research is in the pedagogy of computational thinking. He is part of a multi-disciplinary team that seeks to introduce computational thinking to high school students through app development in STEM courses.
Industry Expertise
Areas of Expertise
Education
Vanderbilt University
Ph.D.
Computer Science
1995
Indian Institute of Science
M.E.
Computer Science
1990
Andhra University
B.E.
Computer Science
1989
Articles
A Semi-supervised Learning Approach to Differential Privacy
IEEE 13th International Conference on Data Mining Workshops (ICDMW)2013
Motivated by the semi-supervised model in the data mining literature, we propose a model for differentially-private learning in which private data is augmented by public data to achieve better accuracy. Our main result is a differentially private classifier with significantly improved accuracy compared to previous work. We experimentally demonstrate that such a classifier produces good prediction accuracies even in those situations where the amount of private data is fairly limited. This expands the range of useful applications of differential privacy since typical results in the differential privacy model require large private data sets to obtain good accuracy.
Communication-Efficient Privacy-Preserving Clustering
Transactions on Data Privacy2010
The ability to store vast quantities of data and the emergence of high speed networking have led to intense interest in distributed data mining. However, privacy concerns, as well as regulations, often prevent the sharing of data between multiple parties. Privacy-preserving distributed data mining allows the cooperative computation of data mining ...
A Practical Differentially Private Random Decision Tree Classifier
IEEE International Conference on Data Mining Workshops (ICDMW)2009
In this paper, we study the problem of constructing private classifiers using decision trees, within the framework of differential privacy. We first construct privacy-preserving ID3 decision trees using differentially private sum queries. Our experiments show that for many data sets a reasonable privacy guarantee can only be obtained via this method at a steep cost of accuracy in predictions. We then present a differentially private decision tree ensemble algorithm using the random decision tree approach. We demonstrate experimentally that our approach yields good prediction accuracy even when the size of the datasets is small. We also present a differentially private algorithm for the situation in which new data is periodically appended to an existing database. Our experiments show that our differentially private random decision tree classifier handles data updates in a way that maintains the same level of privacy guarantee.
Sum-of-Squares Heuristics for Bin Packing and Memory Allocation
Journal of Experimental Algorithmics2008
The sum-of-squares algorithm (SS) was introduced by Csirik, Johnson, Kenyon, Shor, and Weber for online bin packing of integral-sized items into integral-sized bins. First, we show the results of experiments from two new variants of the SS algorithm. The first variant, which runs in time O(n&sqrt;BlogB), appears to have almost identical expected waste ...
A Secure Clustering Algorithm for Distributed Data Streams
IEEE 7th International Conference on Data Mining Workshops (ICDMW)2007
We present a distributed privacy-preserving protocol for the clustering of data streams. The participants of the se- cure protocol learn cluster centers only on completion of the protocol. Our protocol does not reveal intermediate candidate cluster centers. It is also efficient in terms of communication. The protocol is based on a new memory- efficient clustering algorithm for data streams. Our experi- ments show that, on average, the accuracy of this algorithm is better than that of the well known k-means algorithm, and compares well with BIRCH, but has far smaller mem- ory requirements.
