doi: 10.7763/IJMLC.2012.V2.225
Personalized Privacy Preserving Publication of Transactional Datasets Using Concept Learning
S. Ram Prasad Reddy1
,
Kvsvn Raju2,3 ,
V. Valli Kumari4
- 1Computer Science Engineering Department, Vignan’s Institute of Engineering for Women, Visakhpatnam-530049, Andhra Pradesh, India
- 2Andhra University, Visakhapatnam-530003, Andhra Pradesh, India
- 3R&D, ANITS, Visakhapatnam, India
- 4Computer Science and Systems Engineering Department, Andhra University, Visakhapatnam-530003, Andhra Pradesh, India
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.
Keywords
- Privacy preserving
- hashing
- anonymity
- concept learning
- transactional datasets
How to 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, vol. 2, no. 6, pp. 733-737, 2012. https://doi.org/10.7763/IJMLC.2012.V2.225
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).