Injecting random noise into model training is a proven technique for reducing the likelihood that sensitive data is retrievable. The addition of noise makes it harder for an attacker to deduce precise values or to reconstruct original training elements by examining outputs. Some may believe limiting dataset export or providing wider access for rigorous testing helps, but they do not specifically address an attacker’s ability to extract hidden information. Saving logs on a separate medium assists with operational visibility, yet does not inherently secure the underlying model from extraction attempts.
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