The Y.I.N. Mazari Ordering: A Necessary Primitive for verifiable differential Privacy in Federated Learning

Abstract

We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning