Papers Papers/2020



Paper Unlock

Authors: Montserrat Batet ; Arnau Erola ; David Sánchez and Jordi Castellà-Roca

Affiliation: Universitat Rovira i Virgili, Spain

ISBN: 978-989-758-015-4

ISSN: 2184-433X

Keyword(s): Data Semantics, Set-valued Data, Privacy, Microaggregation, Knowledge Bases.

Related Ontology Subjects/Areas/Topics: Agents ; Applications ; Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Enterprise Ontologies ; Formal Methods ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Ontologies ; Pattern Recognition ; Privacy, Safety and Security ; Sensor Networks ; Signal Processing ; Simulation and Modeling ; Soft Computing ; Symbolic Systems

Abstract: It is quite common that companies and organisations require of releasing and exchanging information related to individuals. Due to the usual sensitive nature of these data, appropriate measures should be applied to reduce the risk of re-identification of individuals while keeping as much data utility as possible. Many anonymisation mechanisms have been developed up to present, even though most of them focus on structured/relational databases containing numerical or categorical data. However, the anonymisation of transactional data, also known as set-valued data, has received much less attention. The management and transformation of these data presents additional challenges due to their variable cardinality and their usually textual and unbounded nature. Current approaches focusing on set-valued data are based on the generalisation of original values; however, this suffers from a high information loss derived from the reduced granularity of the output values. To tackle this problem, in this paper we adapt a well-known microaggregation anonymisation mechanism so that it can be applied to textual set-valued data. Moreover, since the utility of textual data is closely related to their meaning, special care has been put in preserving data semantics. To do so, appropriate semantic similarity and aggregation functions are proposed. Experiments conducted on a real set-valued data set show that our proposal better preserves data utility in comparison with non-semantic approaches. (More)


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Batet, M.; Erola, A.; Sánchez, D. and Castellà-Roca, J. (2014). Semantic Anonymisation of Set-valued Data. In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4 ISSN 2184-433X, pages 102-112. DOI: 10.5220/0004811901020112

author={Montserrat Batet. and Arnau Erola. and David Sánchez. and Jordi Castellà{-}Roca.},
title={Semantic Anonymisation of Set-valued Data},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},


JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Semantic Anonymisation of Set-valued Data
SN - 978-989-758-015-4
IS - 2184-433X
AU - Batet, M.
AU - Erola, A.
AU - Sánchez, D.
AU - Castellà-Roca, J.
PY - 2014
SP - 102
EP - 112
DO - 10.5220/0004811901020112

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.