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

2025/11/03 by Wagh, Sameer, Stibler, Kenneth, Gupta, Shubham +8
Computer Science · #Cryptography and Data Security #Privacy-Preserving Technologies in Data #Blockchain Technology Applications and Security

paper · doi:10.48550/arxiv.2511.02055

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.

Citations

• Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling
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• MapReduce

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