Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/12/4/71 |
| _version_ | 1797570435978625024 |
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| author | Nancy Awad Jean-Francois Couchot Bechara Al Bouna Laurent Philippe |
| author_facet | Nancy Awad Jean-Francois Couchot Bechara Al Bouna Laurent Philippe |
| author_sort | Nancy Awad |
| collection | DOAJ |
| description | 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 <inline-formula> <math display="inline"> <semantics> <msup> <mi>k</mi> <mi>m</mi> </msup> </semantics> </math> </inline-formula>-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. |
| first_indexed | 2024-03-10T20:24:33Z |
| format | Article |
| id | doaj.art-ca8df5621635437da64a4d55694a19f9 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T20:24:33Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-ca8df5621635437da64a4d55694a19f92023-11-19T21:55:59ZengMDPI AGFuture Internet1999-59032020-04-011247110.3390/fi12040071Publishing Anonymized Set-Valued Data via Disassociation towards AnalysisNancy Awad0Jean-Francois Couchot1Bechara Al Bouna2Laurent Philippe3Femto-ST Institute, UMR 6174 CNRS, University of Bourgogne-Franche-Comte, 25000 Besançon, FranceFemto-ST Institute, UMR 6174 CNRS, University of Bourgogne-Franche-Comte, 25000 Besançon, FranceTICKET Labortary, Antonine University, Hadat-Baabda 1003, LebanonFemto-ST Institute, UMR 6174 CNRS, University of Bourgogne-Franche-Comte, 25000 Besançon, FranceData 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 <inline-formula> <math display="inline"> <semantics> <msup> <mi>k</mi> <mi>m</mi> </msup> </semantics> </math> </inline-formula>-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.https://www.mdpi.com/1999-5903/12/4/71anonymizationknowledge extractionant colony clusteringassociation rulesutilityprivacy |
| spellingShingle | Nancy Awad Jean-Francois Couchot Bechara Al Bouna Laurent Philippe Publishing Anonymized Set-Valued Data via Disassociation towards Analysis Future Internet anonymization knowledge extraction ant colony clustering association rules utility privacy |
| title | Publishing Anonymized Set-Valued Data via Disassociation towards Analysis |
| title_full | Publishing Anonymized Set-Valued Data via Disassociation towards Analysis |
| title_fullStr | Publishing Anonymized Set-Valued Data via Disassociation towards Analysis |
| title_full_unstemmed | Publishing Anonymized Set-Valued Data via Disassociation towards Analysis |
| title_short | Publishing Anonymized Set-Valued Data via Disassociation towards Analysis |
| title_sort | publishing anonymized set valued data via disassociation towards analysis |
| topic | anonymization knowledge extraction ant colony clustering association rules utility privacy |
| url | https://www.mdpi.com/1999-5903/12/4/71 |
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