Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition

The Edgeworth Accountant introduces an analytical method to efficiently compute overall privacy loss under composition in differential privacy, using the $f$-differential privacy framework and Edgeworth expansion. This approach provides non-asymptotic $(\epsilon, \delta)$ 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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