The ongoing evolution of privacy-enhancing technologies (PETs) in the context of AI and machine learning heralds a future where user privacy and system efficiency coexist seamlessly. Techniques such as secure multi-party computation, which allows data to be processed in fragments among various parties without revealing the underlying data to each other, are continually improving. As these technologies mature, the trade-off between privacy protection and operational efficiency is expected to diminish.
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