CORDIS - Resultados de investigaciones de la UE

KIProtect: The security layer for data science and artificial intelligence

Periodic Reporting for period 1 - KIProtect (KIProtect: The security layer for data science and artificial intelligence)

Período documentado: 2019-02-01 hasta 2019-05-31

The amount of sensitive and personal data increases rapidly due to the spread of digital technologies throughout society. The insights that can be extracted from all this data offer great opportunities for improving our quality of life and for developing a data driven society. However, large-scale collection of personal and sensitive data also poses great risks due to the inherent potential for surveillance and abuse. Minimizing these risks requires a clear legal framework like the GDPR that can regulate the use of such data, as well as robust and scalable technological solutions that can reliably protect the data. With KIProtect we want to provide such a solution and help organizations to build a modern, secure infrastructure for processing sensitive data. We aim to provide open, extensible software tools that can be easily used in modern data infrastructures, regardless whether data processing takes places in the cloud or on premise. We believe that sensitive data should be protected in real-time and directly where and when it gets created, and we want to provide tooling that makes this possible and straightforward. We want to offer strong, mathematically secure methods for anonymization, encryption and pseudonymization of sensitive data. By doing this we want to make it easy for organizations to manage sensitive data and share it securely with partners. We believe that real-time data sharing can enable data-driven decision making for businesses, civic organizations and governments, and we want to ensure that such data sharing can be done in a straightforward and secure way. To make this possible, we plan to release a suite of open-source tools for protecting sensitive data, as well as enterprise-grade solutions for securely managing and sharing such data.
We conducted several proof-of-concept (PoC) projects with customers from different industry segments in order to evaluate the feasibility of different ways to identify and protect sensitive data. Notably, we investigated methods for structure- and format-preserving pseudonymization and real-time anonymization of sensitive data, as well as methods for discovery of sensitive information in unstructured data. We used the insights that we've gained from these PoC projects and the solutions that we developed to further refine and adapt our business plan and improve our understanding of core customer needs.
We developed several novel approaches to real-time pseudonymization and anonymization of sensitive data, as well as methods for the detection of personal and sensitive information in unstructured data. We plan to publish our research findings and develop the methods into enterprise-grade solutions that can be used for protecting sensitive information in real-world use cases. We think that our approach to data security and data protection can play a significant role for the enablement of new digital economic processes and accelerate the shift to a data-driven society. By providing a trust and security layer for sensitive data, we make it easy for organizations to securely work with such data and share it with partners. This in turn enables the analysis of the data and the realization of positive effects for society. As a concrete first step, we are releasing a set of open-source tools based on our research findings that can be used to identify and protect sensitive data in real-time.
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