Researchers at MIT have developed a new privacy technique that protects sensitive data while maintaining a machine-learning model's performance. The technique called Probably Approximately Correct (PAC) Privacy involves adding the smallest amount of noise possible to ensure data privacy. The framework can be used for different types of models and applications without needing knowledge of the model's inner workings or training process. The technique also requires less noise than other approaches, making it easier to create machine-learning models that hide training data while maintaining accuracy in real-world settings.
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