Can you explain what differential privacy is in the context of AI?
Differential privacy is a technique used to ensure that the privacy of individuals in a dataset is protected when data is analyzed or shared.
How does it work? What are the main principles behind it?
It works by adding noise to the data or the results of queries, making it difficult to identify individual data points while still allowing for useful insights.
What are some real-world applications of differential privacy?
It's used in various fields, including healthcare for patient data protection, and in tech companies like Google and Apple to enhance user privacy.
Are there any limitations to using differential privacy?
Yes, while it provides strong privacy guarantees, it can also reduce the accuracy of the data analysis if not implemented carefully.
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