The Edgeworth Accountant introduces an analytical method to efficiently compute overall privacy loss under composition in differential privacy, using the -differential privacy framework and Edgeworth expansion. This approach provides non-asymptotic bounds without increasing computational cost, making it particularly useful for developers working on privacy-preserving deep learning and federated analytics systems.
Read the full article at arXiv cs.CR (Cryptography & Security)
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