Title

On privacy-aware eScience workflows

Document Type

Article

Source of Publication

Computing

Publication Date

5-1-2020

Abstract

© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons’ medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. 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 with 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 solution privacy requirements of datasets used and generated by a workflow execution. We also present a technique for anonymizing workflow data given an anonymity degree.

ISSN

0010-485X

Publisher

Springer

Volume

102

Issue

5

First Page

1171

Last Page

1185

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

e-Science, Privacy, Workflow

Scopus ID

85078601697

Indexed in Scopus

yes

Open Access

no

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