CORDIS Project
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This project focuses on enhancing privacy in machine learning through secure computation techniques. It aims to protect sensitive data during collaborative learning processes, ensuring that data remains confidential while still enabling effective model training.
Machine learning algorithms are data-hungry, and perform better when exposed to more and more data.
Such data is being collected in massive amounts by internet giants, and is often sensitive and private.
Examples include the purchases and browsing history of users, their health data and exercise activity, locations they travel to and messages they type into their mobile phone.
The amount of data being collected can be significantly reduced using cryptographic techniques, in particular, using sec…
BAR ILAN UNIVERSITY
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