The concept of differential privacy has gained momentum in the tech industry, especially among female professionals concerned with safeguarding data. This technique injects a certain amount of randomness into the data, ensuring that individual privacy is maintained even while the overall data set remains useful for analysis. Differential privacy is increasingly becoming a standard part of the data anonymization toolkit, balancing data utility and privacy.
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