As Artificial Intelligence (AI) integrates into diverse areas, particularly
in content generation, ensuring rightful ownership and ethical use becomes
paramount. AI service providers are expected to prioritize responsibly sourcing
training data and obtaining licenses from data owners. However, existing
studies primarily center on safeguarding static copyrights, which simply treats
metadata/datasets as non-fungible items with transferable/trading capabilities,
neglecting the dynamic nature of training procedures that can shape an ongoing
trajectory.
In this paper, we present \textsc{IBis}, a blockchain-based framework
tailored for AI model training workflows. \textsc{IBis} integrates on-chain
registries for datasets, licenses and models, alongside off-chain signing
services to facilitate collaboration among multiple participants. Our framework
addresses concerns regarding data and model provenance and copyright
compliance. \textsc{IBis} enables iterative model retraining and fine-tuning,
and offers flexible license checks and renewals. Further, \textsc{IBis}
provides APIs designed for seamless integration with existing contract
management software, minimizing disruptions to established model training
processes. We implement \textsc{IBis} using Daml on the Canton blockchain.
Evaluation results showcase the feasibility and scalability of \textsc{IBis}
across varying numbers of users, datasets, models, and licenses.

通过区块链技术构建的基于数据集、许可证和模型的注册库的人工智能模型训练框架 (IBis)，解决了数据和模型来源、版权合规等问题，实现了迭代模型训练和灵活的许可证检查和续约。

AI 的版权、来源和血统：利用区块链

Is Your AI Truly Yours? Leveraging Blockchain for Copyrights,  Provenance, and Lineage

Federated Learning (FL) enables multiple parties to distributively train a ML
model without revealing their private datasets. However, it assumes trust in
the centralized aggregator which stores and aggregates model updates. This
makes it prone to gradient tampering and privacy leakage by a malicious
aggregator. Malicious parties can also introduce backdoors into the joint model
by poisoning the training data or model gradients. To address these issues, we
present BEAS, the first blockchain-based framework for N-party FL that provides
strict privacy guarantees of training data using gradient pruning (showing
improved differential privacy compared to existing noise and clipping based
techniques). Anomaly detection protocols are used to minimize the risk of
data-poisoning attacks, along with gradient pruning that is further used to
limit the efficacy of model-poisoning attacks. We also define a novel protocol
to prevent premature convergence in heterogeneous learning environments. We
perform extensive experiments on multiple datasets with promising results: BEAS
successfully prevents privacy leakage from dataset reconstruction attacks, and
minimizes the efficacy of poisoning attacks. Moreover, it achieves an accuracy
similar to centralized frameworks, and its communication and computation
overheads scale linearly with the number of participants.

BEAS 是第一个基于区块链的 N-party Federated Learning 框架，使用梯度剪枝技术提供严格的隐私保障，减少了数据投毒和模型投毒攻击，并采用一种新的协议来防止异构学习环境下的早期收敛。在多个数据集上进行了广泛的实验，显示出与集中式框架相似的准确性和线性的通信和计算成本。

BEAS：区块链支持的异步安全联邦机器学习

BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine  Learning

Metaverse has recently attracted paramount attention due to its potential for
future Internet. However, to fully realize such potential, Metaverse
applications have to overcome various challenges such as massive resource
demands, interoperability among applications, and security and privacy
concerns. In this paper, we propose MetaChain, a novel blockchain-based
framework to address emerging challenges for the development of Metaverse
applications. In particular, by utilizing the smart contract mechanism,
MetaChain can effectively manage and automate complex interactions among the
Metaverse Service Provider (MSP) and the Metaverse users (MUs). In addition, to
allow the MSP to efficiently allocate its resources for Metaverse applications
and MUs' demands, we design a novel sharding scheme to improve the underlying
blockchain's scalability. Moreover, to leverage MUs' resources as well as to
attract more MUs to support Metaverse operations, we develop an incentive
mechanism using the Stackelberg game theory that rewards MUs' contributions to
the Metaverse. Through numerical experiments, we clearly show the impacts of
the MUs' behaviors and how the incentive mechanism can attract more MUs and
resources to the Metaverse.

本文提出了 MetaChain，这是一个基于区块链的框架，旨在通过利用智能合约机制、设计分片机制以提高扩展性和开发激励机制来解决 Metaverse 应用面临的挑战。通过数字实验，展示了开发激励机制对引导更多用户和资源，促进 Metaverse 的运营的作用。