IADIS International Journal on Computer Science and Information Systems

Published by IADIS (International Association for Development of the Information Society) • ISSN (Online): 1646-3692 • ISSN (Print): 1646-3692
100% Open Access
Double-Blind Peer Review
Crossref DOI Persistent IDs
Open Access Peer-Reviewed Original Research

Assuring Data Privacy with Privas – a Tool for Data Publishers

Joana Margarida Miguel *
Maria JoĂŁo Varanda Pereira *
Pedro Rangel Henriques *
Mario BerĂłn *
* 1Centro ALGORITMI – Universidade do Minho, Portugal 2CeDRI – Instituto Politécnico de Bragança, Portugal 3University of San Luis, Argentina (Portugal)
* 1Centro ALGORITMI – Universidade do Minho, Portugal 2CeDRI – Instituto Politécnico de Bragança, Portugal 3University of San Luis, Argentina (Portugal)
* 1Centro ALGORITMI – Universidade do Minho, Portugal 2CeDRI – Instituto Politécnico de Bragança, Portugal 3University of San Luis, Argentina (Portugal)
* 1Centro ALGORITMI – Universidade do Minho, Portugal 2CeDRI – Instituto Politécnico de Bragança, Portugal 3University of San Luis, Argentina (Portugal)

Abstract

The technology of nowadays allows to easily extract, store, process and use information about individuals and organizations. The increase of the amount of data collected and its value to our society was, at first, a great advance that could be used to optimize processes, find solutions and support decisions but also brought new problems related with lack of privacy and malicious attacks to confidential information. In this paper, a tool to anonymize datab ases is presented. It can be used by data publishers to protect information from attacks controlling the desired privacy level and the data usefulness. In order to specify these requirements a DSL ( PrivasL) is used and the automatization of repository tran sformation, that is based on language processing techniques, is the novelty of this work.

Keywords

Privacy Repositories PPDP Anonymization DSL
Full-Text PDF Available

Read Complete Peer-Reviewed Manuscript

Includes full econometric models, data tables, policy recommendations, declarations, and citations.

Declarations & Ethics

Funding: This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest: The authors declare no competing financial or institutional interests.
Peer Review: Double-blind peer reviewed by international subject specialists.
License: Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
APA / MLA / BibTeX
Miguel, et al. (2019). Assuring Data Privacy with Privas – a Tool for Data Publishers. IADIS International Journal on Computer Science and Information Systems, 14(2). https://doi.org/10.33965/ijcsis_2019_v14i2_04
Miguel, et al. "Assuring Data Privacy with Privas – a Tool for Data Publishers." IADIS International Journal on Computer Science and Information Systems, vol. 14, no. 2, 2019. https://doi.org/10.33965/ijcsis_2019_v14i2_04
Miguel, et al. "Assuring Data Privacy with Privas – a Tool for Data Publishers." IADIS International Journal on Computer Science and Information Systems 14, no. 2 (2019). https://doi.org/10.33965/ijcsis_2019_v14i2_04