With the ever-increasing amount of personal data that is being collected, shared and analyzed, data privacy continues to be a big concern. Managing and sharing sensitive data that contains personally identifiable information is a highly challenging task that involves ethical, legal, and technical aspects. Governments, researchers, and businesses often use sensitive data of one type or another in order to facilitate research or create business value and are required to do so in strict compliance with data protection legislation such as GDPR.
To tackle the technical side of privacy compliance, numerous Privacy-Enhancing Technologies (PETs) such as Differential Privacy, Federated Learning, Homomorphic Encryption, and others have emerged, which enable the processing and sharing of sensitive data in a way that would allow deriving value from it but would not compromise data privacy.
Nonetheless, many organizations are still reluctant to use modern PETs due to their complexity and lack of explicit requirements from the legislators as to which technologies specifically must be used. As a result, the majority of PETs remain unfamiliar to most engineers and there is a substantial risk of PETs becoming useless unless clear and meaningful guidance is offered to organizations [1].
In this project, we analyze the challenges organizations face in the process of implementing technical measures for data privacy compliance and develop continuing learning education materials on Privacy-Enhancing Technologies, with the aim to support their adoption in organizations.
Motivated by the above industrial challenges, the objective of this project is three-fold:
The results of the project are available as concise and engaging learning materials at privacyeducation.tech.
[1] “GDPR Challenges,” https://www.pdp4e-project.eu/gdpr-challenges/, accessed: 2021-08-27.
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