Blockchain-Based Platform for Trusted Collaborations on Data and AI Models

Kalapriya Kannan, Abhishek Singh, Mudit Verma, P. Jayachandran, S. Mehta
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引用次数: 0

Abstract

Data analytics and artificial intelligence are extensively used by enterprises today and they increasingly span organization boundaries. Such collaboration between organizations today happens in an ad hoc manner, with very little visibility and systemic control on who is accessing the data, how, and for what purpose. When sharing data and AI models with other organizations, the owners desire the ability to control access, have visibility into the entire data pipeline and lineage, and ensure integrity. In this work, we present a decentralized trusted data and model platform for collaborative AI, that leverages blockchain as an immutable metadata store of data and model resources and operations performed on them, to support and enforce ownership, authenticity, integrity, lineage and auditability properties. Smart contracts enforce policies specified on data, including hierarchical and composite policies that are uniquely enabled by the use of blockchain. We demonstrate that our system is light-weight and can support over 1000 transactions per second with sub-second latency, significantly lower than the time taken to execute data pipelines.
基于区块链的数据和人工智能模型可信协作平台
数据分析和人工智能在当今的企业中被广泛使用,它们越来越多地跨越组织边界。今天,组织之间的这种协作以一种特别的方式发生,对于谁访问数据、如何访问以及出于什么目的访问数据,几乎没有可见性和系统控制。当与其他组织共享数据和人工智能模型时,所有者希望能够控制访问,对整个数据管道和沿路具有可见性,并确保完整性。在这项工作中,我们为协作人工智能提供了一个分散的可信数据和模型平台,该平台利用区块链作为数据和模型资源的不可变元数据存储以及对其执行的操作,以支持和执行所有权,真实性,完整性,血统和可审计性属性。智能合约执行数据上指定的策略,包括使用区块链唯一启用的分层和复合策略。我们证明了我们的系统是轻量级的,每秒可以支持超过1000个事务,延迟时间低于次秒,大大低于执行数据管道所花费的时间。
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