Title

Privacy-preserving data analysis workflows for eScience

Document Type

Conference Proceeding

Source of Publication

CEUR Workshop Proceedings

Publication Date

1-1-2019

Abstract

©2019 Copyright held by the author(s). Computing-intensive experiences in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era’s challenges. These experiences are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) sensitive data like persons’ past diseases and treatments. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists identifying the datasets, generated by the workflows, that need to be anonymized along with setting the anonymization degree that must be met. We present in this paper a preliminary for setting and inferring anonymization requirements of datasets used and generated by a workflow execution. The approach was implemented and showcased using a concrete example, and its efficiency assessed through validation exercises.

ISSN

1613-0073

Publisher

CEUR-WS

Volume

2322

Disciplines

Computer Sciences

Keywords

Anonymization; Data driven; Its efficiencies; Modern science; Privacy preserving; Sensitive datas; Work-flows; Workflow execution

Scopus ID

85062661645

Indexed in Scopus

yes

Open Access

no

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