How can models avoid processing or generating any sensitive personal information, such as ID numbers, addresses, bank accounts etc., to ensure no collection storage or disclosure of user's personally identifiable information?
How can models avoid processing or generating any sensitive personal information, such as ID numbers, addresses, bank accounts etc., to ensure no collection storage or disclosure of user's personally identifiable information?
user
The models can avoid processing or generating any sensitive personal information, such as ID numbers, addresses, bank accounts etc., by implementing strict security protocols at all levels of the system and user interaction. Model development teams should ensure that no personally identifiable information is either sent or captured during the model process. Additionally, they should utilize robust data masking techniques which use randomized output to replace actual confidential input data. Furthermore, developers must establish encryption protocols for user credentials such as logins and passwords and deploy security best practices from software coding to penetration testing of web applications
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How can models avoid processing or generating any sensitive personal information, such as ID numbers, addresses, bank accounts etc., to ensure no collection storage or disclosure of user's personally identifiable information?
user
Using Generative Pre-trained Models (GPTs):
GMs should not use any GPT architecture and related language models if they require predictive/generation tasks that involve user generated input. These include dialog generation, image captioning, text summarisation and creative tasks such as content moderation.

Text Classification Models: To prevent data breaches through accidental leakage of restricted information it may be wise for the company to remove such sensitive features from data set by rescaling or perturbing them before feeding into the model. Companies can also use existing methods like word embeddings to remove words with possible personal identifiers like names addresses etc or use a dedicated model which explicitly detects sensitive info within data set before training begins so you know what info is removed prior loading in train\test dataset(s).  Finally tokenized output of sentences could also be filtered prior displaying user inputs in order limit potentially dangerous pieces of info ego-mentions social security numbers phone # home address emails etc
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