Confidentiality-maintaining artificial intelligence: learning from encoded information

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Amid the age of Synthetic Intelligence (AI) and vast data, prophetic models have emerged as a imperative tool over diverse enterprises counting healthcare, finance, and genomics. These models lean intensely on the processing of sensitive data making data confidentiality a vital concern. The main challenge lies in maximizing data effectiveness without compromising the privacy and genuineness of the data involved. Attaining this equilibrium is crucial for the sustained advancement and approval of AI technologies.

Jordan Fréry

Machine Learning Tech Lead at Zama.

Cooperation and open origin

Crafting a resilient dataset for educating machine learning models presents notable obstacles. For example, while AI technologies such as ChatGPT have flourished by accumulating vast amounts of data obtainable on the internet, healthcare data cannot be assembled this liberally due to confidentiality concerns. Constructing a healthcare dataset involves the integration of data from multiple sources including physicians, hospitals, and across boundaries.

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