For unsupervised learning, commonly used for clustering, association, or dimensionality reduction, features don't need to be fully independent, but if you have many unique values it's likely that your models can learn to differentiate on these high entropy values instead of learning interesting or significant things as you might hope.
If you're working on generative unsupervised models, for customers, I cannot express how much risk this may create, for security and secret disclosure, for Oracle Cloud Infrastructure (OCI) customers. Generative models are premised on regurgitating their inputs, and thousands of papers have been written on getting private information back out of trained models.
It's not clear what problem you're working on, and the question seems early in its formulation.
I recommend you spend time delving into the limits of statistics and data science, which are the foundation of modern popular machine learning methods.
Once you have an idea of what questions can be answered well by ML, and what can't, then you might consider something like fastAI's course.
https://towardsdatascience.com/the-actual-difference-between-statistics-and-machine-learning-64b49f07ea3
https://www.nature.com/articles/nmeth.4642
Again, depending on how the outputs will be used or who can view or (even indirectly) query the model, it seems unwise to train on private values, especially if you want to generate outputs. ML methods are only useful if you have access to a lot of data, and if you have access to the data of many users, you need to be good steward of Oracle Cloud customer data.
