Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets
Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets
复制标题
用于人工智能资产来源追踪的分布式账本
DOI：
10.1007/978-3-030-42504-3_26
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Marcel Gygli
中科院分区：
文献类型：
--
作者：
Philipp Lüthi;Thibault Gagnaux;Marcel Gygli
数据的高可用性是人工智能 (AI) 和机器学习 (ML) 的当前趋势的原因。然而，由于缺乏信任和担心失去控制，参与者之间不愿意共享高级数据集。来源追踪系统是通过提高透明度来建立信任的一种可能措施。尤其是沿着完整的人工智能价值链追踪人工智能资产，面临着信任、隐私、保密、可追溯、公平报酬等各种挑战。在本文中，我们为人工智能资产及其在人工智能价值链中的关系设计了一个基于图的起源模型。此外，我们提出了一种将人工智能资产安全地交换给选定各方的协议。然后将来源模型和交换协议组合起来，并作为无需许可的区块链上的智能合约来实现。我们展示了智能合约如何在现有行业用例中追踪人工智能资产，同时解决所有挑战。因此，我们的智能合约有助于提高可追溯性和透明度，鼓励参与者之间的信任，从而促进他们之间的合作。
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.
DOI：
10.1145/3176258.3176333
发表时间：
2018-03
期刊：
Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy
影响因子：
--
作者：
A. Ramachandran;Murat Kantarcioglu
通讯作者：
A. Ramachandran;Murat Kantarcioglu
影响因子：
1.3
作者：
Stauder;Ostler;Wilhelm;Koller;Kranzfelder
通讯作者：
Kranzfelder
DOI：
10.1002/jsc.2148
发表时间：
2017
期刊：
Strategic Change
影响因子：
--
作者：
Maull R
通讯作者：
Maull R