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Aris Gkoulalas-Divanis
Researcher at IBM
Publications - 111
Citations - 2919
Aris Gkoulalas-Divanis is an academic researcher from IBM. The author has contributed to research in topics: Information privacy & Data publishing. The author has an hindex of 26, co-authored 109 publications receiving 2623 citations. Previous affiliations of Aris Gkoulalas-Divanis include University of Thessaly & Research Academic Computer Technology Institute.
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Journal ArticleDOI
Semantic trajectories modeling and analysis
Christine Parent,Stefano Spaccapietra,Chiara Renso,Gennady Andrienko,Natalia Andrienko,Vania Bogorny,Maria Luisa Damiani,Aris Gkoulalas-Divanis,José Antônio Fernandes de Macêdo,Nikos Pelekis,Yannis Theodoridis,Zhixian Yan +11 more
TL;DR: A survey of the approaches and techniques for constructing trajectories from movement tracks, enriching trajectories with semantic information to enable the desired interpretations of movements, and using data mining to analyze semantic trajectories to extract knowledge about their characteristics.
Journal ArticleDOI
Publishing data from electronic health records while preserving privacy: a survey of algorithms.
TL;DR: This work presents a survey of algorithms that have been proposed for publishing structured patient data, in a privacy-preserving way, and derives insights on their operation, and highlights their advantages and disadvantages.
Journal ArticleDOI
Anonymization of electronic medical records for validating genome-wide association studies
TL;DR: The approach automatically extracts potentially linkable clinical features and modifies them in a way that they can no longer be used to link a genomic sequence to a small number of patients, while preserving the associations between genomic sequences and specific sets of clinical features corresponding to GWAS-related diseases.
Proceedings ArticleDOI
An integer programming approach for frequent itemset hiding
TL;DR: This paper proposes a novel, exact algorithm for association rule hiding and evaluates it on real world datasets demonstrating its effectiveness towards solving the problem of securing sensitive knowledge from being exposed in patterns extracted during association rule mining.
Journal ArticleDOI
Exact Knowledge Hiding through Database Extension
TL;DR: Extending the original database for sensitive itemset hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality.