%0 Conference Proceedings
%T Evaluating the Privacy Implications of Frequent Itemset Disclosure
%+ Boise State University
%+ Rutgers University [Camden]
%A Serra, Edoardo
%A Vaidya, Jaideep
%A Akella, Haritha
%A Sharma, Ashish
%Z Part 8: Privacy
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 32th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC)
%C Rome, Italy
%Y Sabrina De Capitani di Vimercati
%Y Fabio Martinelli
%I Springer International Publishing
%3 ICT Systems Security and Privacy Protection
%V AICT-502
%P 506-519
%8 2017-05-29
%D 2017
%R 10.1007/978-3-319-58469-0_34
%K Inverse Frequent itemset Mining
%K Column generation
%Z Computer Science [cs]Conference papers
%X Frequent itemset mining is a fundamental data analytics task. In many cases, due to privacy concerns, only the frequent itemsets are released instead of the underlying data. However, it is not clear how to evaluate the privacy implications of the disclosure of the frequent itemsets. Towards this, in this paper, we define the k-distant-IFM-solutions problem, which aims to find k transaction datasets whose pair distance is maximized. The degree of difference between the reconstructed datasets provides a way to evaluate the privacy risk. Since the problem is NP-hard, we propose a 2-approximate solution as well as faster heuristics, and evaluate them on real data.
%G English
%Z TC 11
%2 https://inria.hal.science/hal-01649007/document
%2 https://inria.hal.science/hal-01649007/file/449885_1_En_34_Chapter.pdf
%L hal-01649007
%U https://inria.hal.science/hal-01649007
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC11
%~ IFIP-SEC
%~ IFIP-AICT-502