Privacy-preserving artificial intelligence: training on encrypted data



In the era of Artificial Intelligence (AI) and big data, predictive models have become an essential tool across various industries including healthcare, finance and genomics. These models rely heavily on the processing of sensitive information making data privacy a critical concern. The key challenge lies in maximizing data utility without compromising the confidentiality and integrity of the information involved. Achieving this balance is essential for the continued advancement and acceptance of AI technologies.

Jordan Fréry

Machine Learning Tech Lead at Zama.

Collaboration and open source

Creating a robust dataset for training machine learning models presents significant challenges. For instance, while AI technologies such as ChatGPT have thrived by gathering vast amounts of data available on the internet, healthcare data cannot be compiled this freely due to privacy concerns. Constructing a healthcare dataset involves the integration of data from multiple sources including doctors, hospitals and across borders.



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