PROVANON

Anonymisation and Provenance Expressing Data Environments with PROV-Turing Pilot Project Final Report
Adriane Chapman, Mark Elliot, Muhammad Aslam Jarwar, Fatemeh Raji, Tom Blount, Duncan Smith
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Modelling data environments within PROV to assist anonymisation decision-making
Published in The 2021 joint UNECE/Eurostat Expert Meeting on Statistical Data Confidentiality
Muhammad Aslam Jarwar, Adriane Chapman, Mark Elliot, Fatemeh Raji
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Provenance, Anonymisation and Data Environments: a Unifying Construction
The Anonymisation Decision-making Framework (ADF) operationalizes the risk management of data exchange between organizations, referred …
Muhammad Aslam Jarwar, Adriane Chapman, Mark Elliot, Fatemeh Raji
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Provenance and Anonymisation (PROVANON)
In this project we are investigating that how the use of provenance formalisms will enable us to capture the necessary features of the complex dynamic data situations to allow more principled analysis of risk, the requirements of anonymisation and related topics such as Cyber Security, privacy and data utility.
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Provenance and Anonymisation (PROVANON)