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: email@example.com).
V. V. Kumari is with Computer Science and Systems Engineering Department, Andhra University Visakhapatnam-530003, Andhra Pradesh, India (e-mail: firstname.lastname@example.org).
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.