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arXiv 2001.09011cs.DCcs.CR
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使用区块链的所有权保护AI市场

Ownership preserving AI Market Places using Blockchain

Nishant Baranwal Somy, Kalapriya Kannan, Vijay Arya, Sandeep Hans, Abhishek Singh, Pranay Lohia, Sameep Mehta

发表信息：
IEEE International Conference on Blockchain, Blockchain 2019
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摘要：

我们提出了一个基于区块链的系统，允许数据所有者，云供应商和AI开发人员在不信任的AI市场中协作训练机器学习模型。数据是一种非常有价值的数字资产，是获取业务洞察的核心。我们的系统使数据所有者能够保留其数据的所有权和隐私，同时仍然允许AI开发人员利用数据进行培训。同样，人工智能开发人员可以利用云计算供应商的计算资源，而不会失去其训练模型的所有权或隐私。我们的系统协议旨在激励所有三个实体-数据所有者，云供应商和AI开发人员在分布式账本上如实记录他们的行为，以便区块链系统提供可验证的不当行为和争议解决证据。我们的系统在Hyperledger Fabric上实现，可以为不保证数据或模型隐私的集中式AI系统提供可行的替代方案。我们给出了实验性能结果，展示了在不同网络配置下其交易的延迟和吞吐量，其中区块链上的对等点可能分布在不同的网络中心和地理位置。我们的研究结果表明，所提出的解决方案可以很好地扩展到大量的数据和模型所有者，并且可以在12个节点的非优化区块链网络上每秒训练多达70个模型，在24个节点的网络中每秒大约训练30个模型。

英文摘要：

We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insights. Our system enables data owners to retain ownership and privacy of their data, while still allowing AI developers to leverage the data for training. Similarly, AI developers can utilize compute resources from cloud vendors without loosing ownership or privacy of their trained models. Our system protocols are set up to incentivize all three entities - data owners, cloud vendors, and AI developers to truthfully record their actions on the distributed ledger, so that the blockchain system provides verifiable evidence of wrongdoing and dispute resolution. Our system is implemented on the Hyperledger Fabric and can provide a viable alternative to centralized AI systems that do not guarantee data or model privacy. We present experimental performance results that demonstrate the latency and throughput of its transactions under different network configurations where peers on the blockchain may be spread across different datacenters and geographies. Our results indicate that the proposed solution scales well to large number of data and model owners and can train up to 70 models per second on a 12-peer non optimized blockchain network and roughly 30 models per second in a 24 peer network.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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