首页> 外文会议>IFIP WG 9. 2, 9. 6/11. 7, 11. 6/SIG 9. 2. 2 International Summer School on Privacy and Identity Management >Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets
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Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

机译:分布式分类帐,用于人工智能资产的出处跟踪

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摘要

High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.
机译:数据的高可用性负责人工智能(AI)和机器学习(ML)的当前趋势。然而,由于缺乏信任和害怕失去控制,高档数据集在演员之间不情愿地共享。来源跟踪系统是通过提高透明度来构建信任的可能措施。特别是沿着完整的AI价值链的AI资产追踪具有各种挑战,例如信任,隐私,机密性,可追溯性和公平薪酬。在本文中,我们为AI资产和AI价值链内的关系设计了一种基于图形的出处模型。此外,我们提出了一份协议,即将AI资产稳定地兑换给选定的缔约方。然后将来源模型和交换协议组合并在许可的区块链上实现为智能合同。我们展示了智能合同如何在解决所有挑战的同时在现有的行业用例中追踪AI资产。因此,我们的智能合同有助于提高可追溯性和透明度,鼓励行动者之间的信任,从而促进他们之间的合作。

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