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This lecture covers the concepts of differential privacy, explaining why it is possible compared to traditional anonymization methods. It delves into the composability of differential privacy, privacy budget management, post-processing immunity, Laplacian distribution, parameter selection, and mechanisms to achieve differential privacy. The exponential mechanism and randomized response techniques are also discussed, along with their differential privacy guarantees and real-world applications in data publishing.
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