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.

翻译：现代人工智能数据经济通过不受控制、缺乏隐私保护且补偿不公的数据提取方式，集中了权力、限制了创新并错配了价值。我们提出私有映射-安全规约（PMSR），这是一种网络原生范式，通过密码学强制的市场机制，将数据经济从掠夺式转变为参与式。PMSR将MapReduce范式扩展至去中心化环境，使计算能够向数据移动，确保可验证的隐私保护、高效的价格发现与激励对齐。实证演示包括大规模推荐系统审计、隐私保护的大语言模型集成（在六个模型上达到87.5%的MMLU准确率）以及跨数百个节点的分布式分析。PMSR为下一代人工智能数据市场建立了可扩展、公平且隐私有保障的基础架构。