What is a method for preserving privacy in AI training and testing data?

Homomorphic encryption is a method that allows computations to be performed on encrypted data without needing to decrypt it first. This ensures that the actual data remains private and secure throughout the training and testing processes of AI models. By utilizing homomorphic encryption, sensitive information can be processed while maintaining confidentiality, which is vital in applications where data privacy is a concern.

In AI training, where large datasets often contain personal data, preserving privacy is essential not just for compliance with regulations like GDPR, but also for maintaining user trust. Homomorphic encryption enables organizations to use valuable data insights while ensuring that the underlying data is never exposed to unauthorized access.

This technique stands apart from other methods like using only public data, which may limit the richness of the training dataset, or open-source algorithms, which don’t inherently provide privacy protection. Additionally, data sharing agreements may facilitate collaboration but do not address how the data itself is handled during processing. In contrast, homomorphic encryption provides a strong technical solution to maintain data confidentiality while still enabling effective AI training and testing.