Differential privacy ensures that aggregate data analysis does not reveal individual user information, protecting user privacy.
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Differential privacy ensures that aggregate data analysis does not reveal individual user information, protecting user privacy.
Differential privacy is a framework in data privacy that aims to provide mathematical guarantees that the inclusion or exclusion of a single individual’s data will not significantly impact the results of aggregate data analysis. This protects the privacy of individual users by ensuring that their data cannot be distinguished or singled out in the aggregated analysis. Differential privacy achieves this by adding noise or randomness to the data in a controlled manner, making it challenging to infer specific individual data points while still allowing for accurate aggregate data analysis.