Mis on homomorfne krüpteerimine ja miks seda ML-s kasutatakse?

Homomorphic encryption is a cryptographic method that allows computations to be performed on encrypted data without decrypting it. The result of these computations, after decryption, matches the result that would be obtained if the computations were performed on the original, unencrypted data.

In the context of machine learning, this is important for ensuring data confidentiality. For example, a model can be trained or predictions can be made on encrypted client data without revealing its content to the server or cloud service.

Example of application:

• The client encrypts their data and sends it to the ML service.
• The service performs calculations (e.g., prediction) on the encrypted data.
• The client decrypts the obtained result and receives the response, without revealing their data.

Thus, homomorphic encryption helps protect the privacy and security of data in ML scenarios.