From the course: Handling Sensitive Data with Cloud and Local AI
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Apply data anonymization for safe AI interactions
From the course: Handling Sensitive Data with Cloud and Local AI
Apply data anonymization for safe AI interactions
Anonymization is the process of protecting personal data, usually by replacing it with placeholder data or anonymous data. Anonymization is the process of protecting personal data, usually by replacing identifying data with non-sensitive placeholders or data. Now, this should be looked at as a piece of a robust solution. Anonymization is pretty effective at protecting personal data. It's probably less effective or not effective when it comes to protecting intellectual property. Now, sometimes implementing anonymization may limit your system's functionality, so it can get in the way of some of the things you try to implement, and that is the trade-off you have to make at times. There are workarounds, and we'll look at some of these workarounds in a little bit. Also, it's good to keep in mind that anonymization can be used in different parts of your AI lifecycle. You can use it on training data and during inference. So you can anonymize a prompt, for example, before you send it to a…
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