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Open AccessArticle

Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

1
Femto-ST Institute, UMR 6174 CNRS, University of Bourgogne-Franche-Comte, 25000 Besançon, France
2
TICKET Labortary, Antonine University, Hadat-Baabda 1003, Lebanon
*
Author to whom correspondence should be addressed.
Future Internet 2020, 12(4), 71; https://doi.org/10.3390/fi12040071
Received: 17 March 2020 / Revised: 14 April 2020 / Accepted: 15 April 2020 / Published: 17 April 2020
(This article belongs to the Special Issue Security and Privacy in Social Networks and Solutions)
Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e.g., discrete locations, search and shopping items) while guaranteeing the confidentiality property known as k m -anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis. In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments. View Full-Text
Keywords: anonymization; knowledge extraction; ant colony clustering; association rules; utility; privacy; disassociation anonymization; knowledge extraction; ant colony clustering; association rules; utility; privacy; disassociation
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Awad, N.; Couchot, J.-F.; Al Bouna, B.; Philippe, L. Publishing Anonymized Set-Valued Data via Disassociation towards Analysis. Future Internet 2020, 12, 71.

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