Proceedings ArticleDOI
A pioneering Cryptic Random Projection based approach for privacy preserving data mining
K. Murugesan,Md. Rukunuddin Ghalib,J. Gitanjali,J. Indumathi,D. Manjula +4 more
- pp 437-439
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TLDR
This paper solves the re-identification quandary (which is found in the conventional random projections) and addresses two kinds of random sequences for generating the random sequences called determinist and indeterminist random sequences and encrypted it in a new way so that the original data cannot be re-identified.Abstract:
Privacy is the most important apprehension in many data mining applications. In this paper a new technique called Cryptic Random Projection, solves the re-identification quandary (which is found in the conventional random projections).Here this encryption based random projection assigns secret keys to the positions of random matrix elements and not to the random numbers. We have addressed two kinds of random sequences for generating the random sequences called determinist and indeterminist random sequences and encrypted it in a new way so that the original data cannot be re-identified. We have also optimized the privacy level which toughens the re-identification of original data without compromising the processing speed and data utility. We hope the projected solution will tarmac way for investigation track and toil well according to the evaluation metrics including hiding effects, data utility, and time performance.read more
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References
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Proceedings ArticleDOI
Database-friendly random projections
TL;DR: This work gives a novel construction of the embedding of k-dimensional Euclidean space, suitable for database applications, which amounts to computing a simple aggregate over k random attribute partitions.
Proceedings ArticleDOI
Very sparse random projections
TL;DR: This paper proposes sparse random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space that multiplies A by a random matrix R in RD x k, reducing the D dimensions down to just k for speeding up the computation.
Proceedings ArticleDOI
Privacy-preserving distributed clustering using generative models
Srujana Merugu,Joydeep Ghosh +1 more
TL;DR: A new measure that quantifies privacy based on information theoretic concepts is proposed, and it is shown that decreasing privacy leads to a higher quality of the combined model and vice versa, and high quality distributed clustering can be achieved with little privacy loss and low communication cost.
Book ChapterDOI
Improving random projections using marginal information
TL;DR: An improved version of random projections that takes advantage of marginal norms, and using a maximum likelihood estimator (MLE), margin-constrained random projections can improve estimation accuracy considerably.
Book ChapterDOI
Distributed Data Mining
TL;DR: The continuous developments in information and communication technology have recently led to the appearance of distributed computing environments, which comprise several, and different sources of large volumes of data and several computing units.