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

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Wagh, Sameer, Stibler, Kenneth, Gupta, Shubham, Strahm, Lacey, Bejan, Irina, Chen, Jiahao, Buckley, Dave, Bhatia, Ruchi, Bandy, Jack, Agarwal, Aayush, Trask, Andrew
Format:	Preprint
Publié:	2025
Sujets:	Cryptography and Security
Accès en ligne:	Acceder al recurso
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author	Wagh, Sameer Stibler, Kenneth Gupta, Shubham Strahm, Lacey Bejan, Irina Chen, Jiahao Buckley, Dave Bhatia, Ruchi Bandy, Jack Agarwal, Aayush Trask, Andrew
author_facet	Wagh, Sameer Stibler, Kenneth Gupta, Shubham Strahm, Lacey Bejan, Irina Chen, Jiahao Buckley, Dave Bhatia, Ruchi Bandy, Jack Agarwal, Aayush Trask, Andrew
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2511_02055
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets Wagh, Sameer Stibler, Kenneth Gupta, Shubham Strahm, Lacey Bejan, Irina Chen, Jiahao Buckley, Dave Bhatia, Ruchi Bandy, Jack Agarwal, Aayush Trask, Andrew Cryptography and Security 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.
title	Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets
topic	Cryptography and Security
url	https://arxiv.org/abs/2511.02055