Home > Archive > 2012 > Volume 2 Number 6 (Dec. 2012) >
IJMLC 2012 Vol.2(6): 733-737 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.225

Personalized Privacy Preserving Publication of Transactional Datasets Using Concept Learning

S. Ram Prasad Reddy, Kvsvn Raju, and V. Valli Kumari

Abstract—In this paper we study the problem of protecting privacy in the publication of transactional data. Consider a collection of transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the point of view of the adversary. We define a new version of the anonymity guarantee using concept learning. Our anonymization model relies on generalization using concept hierarchy and concept learning. The proposed algorithms are experimentally evaluated using real world datasets.

Index Terms—Privacy preserving, hashing, anonymity, concept learning, transactional datasets.

S. R. P. Reddy is with Computer Science Engineering Department, Vignan’s Institute of Engineering for Women, Visakhpatnam-530049, Andhra Pradesh, India (e-mail: reddysadi@ gmail.com).
K. Raju was with Andhra University, Visakhapatnam-530003, Andhra Pradesh, India. He is now with the R&D, ANITS as Director, Visakhapatnam, India (e-mail: kvsvn.raju@gmail.com).
V. V. Kumari is with Computer Science and Systems Engineering Department, Andhra University Visakhapatnam-530003, Andhra Pradesh, India (e-mail: vallikumari@gmail.com).


Cite:S. Ram Prasad Reddy, Kvsvn Raju, and V. Valli Kumari, "Personalized Privacy Preserving Publication of Transactional Datasets Using Concept Learning," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 733-737, 2012.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net
  • APC: 500USD

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