Cryptographic Enhancements for AI Data Sharing Platforms

Abstract

AI data sharing platforms must reconcile two pressures that often clash: the need to exchange high‐value datasets for model development and evaluation, and the obligation to guarantee privacy, integrity, and verifiability of computations on that data. This manuscript surveys and synthesizes cryptographic building blocks—differential privacy, homomorphic encryption, multiparty computation with secure aggregation, zero‐knowledge proofs, attribute-based encryption and proxy re-encryption, trusted execution environments, and domain standards such as Crypt4GH—into a pragmatic, layered architecture for AI data sharing. We outline a methodology that integrates policy-aware access control with threshold key management, private training and inference, verifiable analytics, and auditability. A compact statistical analysis (with an illustrative table) demonstrates how such a stack can bound leakage (ε), preserve utility (accuracy), and manage computational overhead (latency). Results show that a hybrid PETs (privacy-enhancing technologies) approach—combining local differential privacy and secure aggregation for ingestion, homomorphic encryption or TEEs for computation, and zk-proofs for verifiability—achieves strong privacy with modest accuracy loss and acceptable latency for many enterprise scenarios. We conclude with design guidelines and research directions for standards-aligned, future-ready AI data sharing platforms.