Privacy-enhancing computation techniques are gaining traction, providing ways to analyze and share data without exposing individual data points. Secure multi-party computation (SMPC), federated learning, and homomorphic encryption are examples of this trend. These methods allow for data to be processed in a way that never reveals the actual data, a principle that is particularly attractive for female professionals dealing with sensitive information.
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