cape-dataframes VS presidio

- How can we mitigate hallucinations and bias so that we have higher trust in AI generated text?

The features of the Cape API are designed to help solve these problems for developers, and we have a number of early customers using the API in production already.

To get started, checkout our docs: https://docs.capeprivacy.com/

View the API reference: https://api.capeprivacy.com/redoc

Join the discussion on our Discord: https://discord.gg/nQW7YxUYjh

And of course try the CapeChat playground at https://chat.capeprivacy.com/

There are three essential components that enable this: cape encrypt, cape deploy, and cape run. The command cape encrypt encrypts inputs that can be sent into the Cape enclave for processing, cape deploy performs all needed actions for deploying a function into the enclave, and finally cape run invokes the deployed function with an input that was previously encrypted with cape encrypt. Learn more on the Cape docs.

Well, many of the features in this project are simply wrappers around other libraries like this one. Therefore, the value proposition of this project would either have to be the automation aspect or the idea that you can shield the user from the details of how the implemented techniques work. I think both approaches are risky in this setting.

I was wondering what other helpful and easy of use libraries are there for data anonymization like faker and cape-python ?

Perhaps de-identification before training could be helpful here.

Microsoft does seem active in this, e.g. https://microsoft.github.io/presidio/

Something like https://github.com/microsoft/presidio for stripping out PII might fill the role I expected https://github.com/capeprivacy/private-ai to do.

Depending on your use case, you may want to check out Presidio as well. It’s a Microsoft product for PII scrubbing. Perfect for ADF and Synapse pipelines.

There's an API from Microsoft, named Presidio which is used for Anonymization. This is the Github link.