Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets

TL;DR

The paper introduces PMSR, a decentralized, cryptographically secure MapReduce extension that enables privacy-preserving, efficient AI data markets with verifiable privacy and incentivization.

Contribution

It presents PMSR, a novel network-native paradigm extending MapReduce for decentralized AI data markets with cryptographic privacy and economic incentives.

Findings

Achieved 87.5% MMLU accuracy in privacy-preserving LLM ensembling

Demonstrated large-scale recommender audits and distributed analytics

Established a scalable, privacy-guaranteed data market infrastructure

Abstract

The modern AI data economy centralizes power, limits innovation, and misallocates value by extracting data without control, privacy, or fair compensation. We introduce Private Map-Secure Reduce (PMSR), a network-native paradigm that transforms data economics from extractive to participatory through cryptographically enforced markets. Extending MapReduce to decentralized settings, PMSR enables computation to move to the data, ensuring verifiable privacy, efficient price discovery, and incentive alignment. Demonstrations include large-scale recommender audits, privacy-preserving LLM ensembling (87.5\% MMLU accuracy across six models), and distributed analytics over hundreds of nodes. PMSR establishes a scalable, equitable, and privacy-guaranteed foundation for the next generation of AI data markets.