A Dual-Layer Zero-Knowledge Proof Federated Recommendation Algorithm for E-Commerce

Abstract

Centralized e-commerce recommenders face privacy risks, while Federated Recommendation Systems (FRS) suffer from accuracy loss in sparse environments and rely on untrusted aggregators. We propose BL-ZPRS, a framework utilizing bilayer zk-SNARKs for end-to-end trustworthiness. Its lower-layer User-to-Anchor (U2A) paradigm restores collaborative signals via verifiable vectors without exposing raw data, while an upper-layer ZKP proves FedAvg integrity. Evaluations on the Amazon Review dataset show BL-ZPRS achieves accuracy comparable to centralized models with superior resistance to poisoning attacks, effectively balancing privacy and integrity.