Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26691
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: A systematic overview on methods to protect sensitive data provided for various analyses
Authors: Templ, Matthias
Sariyar, Murat
et. al: No
DOI: 10.1007/s10207-022-00607-5
10.21256/zhaw-26691
Published in: International Journal of Information Security
Volume(Issue): 21
Issue: 6
Page(s): 1233
Pages to: 1246
Issue Date: 2022
Publisher / Ed. Institution: Springer
ISSN: 1615-5262
1615-5270
Language: English
Subjects: Anonymization; Privacy-preserving computation; Federated learning; Synthetic data
Subject (DDC): 005: Computer programming, programs and data
Abstract: In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k-anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.
Further description: Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)
URI: https://digitalcollection.zhaw.ch/handle/11475/26691
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering



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