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30 janvier 2018
- handle: 10670/1.t8lcm7
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info:eu-repo/semantics/reference/issn/1867-139X
Ce document est lié à :
info:eu-repo/semantics/reference/issn/1867-8521
https://creativecommons.org/licenses/by/4.0/ , info:eu-repo/semantics/openAccess
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privacy data anonymization pseudonymization de-identification large data-sets identity k-anonymity unicityCiter ce document
Yves-Alexandre de Montjoye et al., « Solving Artificial Intelligence’s Privacy Problem », Field Actions Science Reports, ID : 10670/1.t8lcm7
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Artificial Intelligence (AI) has potential to fundamentally change the way we work, live, and interact. There is however no general AI out there and the accuracy of current machine learning models largely depend on the data on which they have been trained on. For the coming decades, the development of AI will depend on access to ever larger and richer medical and behavioral datasets. We now have strong evidence that the tool we have used historically to find a balance between using the data in aggregate and protecting people’s privacy, de-identification, does not scale to big data datasets. The development and deployment of modern privacy-enhancing technologies (PET), allowing data controllers to make data available in a safe and transparent way, will be key to unlocking the great potential of AI.