Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets
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.
Cite
@article{arxiv.2511.02055,
title = {Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets},
author = {Sameer Wagh and Kenneth Stibler and Shubham Gupta and Lacey Strahm and Irina Bejan and Jiahao Chen and Dave Buckley and Ruchi Bhatia and Jack Bandy and Aayush Agarwal and Andrew Trask},
journal= {arXiv preprint arXiv:2511.02055},
year = {2025}
}