SBTLF：一种使用基于区块链和基于令牌的激励化保护协作机器学习的新方法

人工智能（AI）繁荣的主要问题之一是用于培训AI的数据的隐私。随着国家现在热衷于构建更多量身定制的AI和大型语言模型（LLM），我们如何使用和保护数据变得至关重要。

As nations strive to develop more sophisticated Artificial Intelligence (AI) and large language models (LLM), the privacy of the data used to train these models becomes a primary concern. To address this issue, researchers at the Indian Institute of Technology (IIT) Kharagpur have proposed a novel approach that leverages blockchain technology to facilitate secure data sharing for AI training.

随着各国努力开发更复杂的人工智能（AI）和大型语言模型（LLM），用于训练这些模型的数据的隐私成为主要问题。为了解决这个问题，印度技术学院（IIT）Kharagpur的研究人员提出了一种新颖的方法，该方法利用区块链技术来促进安全数据共享进行AI培训。

Their research focuses on enhancing Federated Learning (FL), a type of machine-learning model, by integrating it with blockchain technology and other privacy measures. This combined approach aims to make machine learning models more secure and equitable for all participants.

他们的研究重点是增强联合学习（FL），即一种机器学习模型，通过将其与区块链技术和其他隐私措施集成在一起。这种合并的方法旨在使机器学习模型对所有参与者更加安全和公平。

At its core, the Federated Learning model is designed to be trained across multiple devices without requiring the transfer of raw data. Similar to how a large project can be divided into smaller tasks to be worked on by individual personal computers, FL enables individual devices (such as smartphones and PCs) to contribute to the training of an AI model without having to share the private data they hold.

从本质上讲，联合学习模型旨在在不需要原始数据传输的情况下对多个设备进行培训。类似于如何将大型项目分为较小的任务，可以由个人计算机来处理，FL使单个设备（例如智能手机和PC）可以为培训AI模型做出贡献，而无需共享他们持有的私人数据。

In FL, each individual device or ‘node’ trains a standard model on its local data and shares only the results—not the actual data—with a central server. To ensure privacy, the data is typically encoded using Local Differential Privacy (LDP). This well-established privacy model adds a layer of 'noise' to the data before it is shared, preserving the privacy of individual information.

在FL中，每个设备或“节点”在其本地数据上训练标准模型，并且仅与中央服务器共享结果（而不是实际数据）。为了确保隐私，通常使用局部微分隐私（LDP）对数据进行编码。这个良好的隐私模型在共享数据之前为数据添加了一层“噪声”，并保留了个人信息的隐私。

In the present study, the researchers introduce a new approach called SBTLF (Secure Blockchain-Based Tokenized LDP Federated Learning). They realized that by combining blockchain technology with federated learning and adding a system of tokens and Local Differential Privacy, they could train machine learning models more securely and efficiently.

在本研究中，研究人员介绍了一种称为SBTLF的新方法（基于安全区块链的LDP联合学习）。他们意识到，通过将区块链技术与联合学习结合并添加一个令牌和当地差异隐私系统，他们可以更安全，有效地训练机器学习模型。

Blockchain technology, which is better known for its association with cryptocurrencies such as Bitcoin, enables users to share data stored as tokens in a ledger in a decentralized and secure manner. For this purpose, the researchers employed HyperLedger Fabric, a specialized blockchain framework designed to securely manage data and transactions among different parties in a federated learning environment. It serves as a distributed ledger that records all the local nodes' updates and helps ensure the transparency of the entire process. This adds a layer of security by preventing any single point of failure, which could occur if only one system were to control the entire process.

区块链技术以与比特币这样的加密货币关联而闻名，它使用户能够以分散且安全的方式共享以代币为代币的数据。为此，研究人员采用了Hyperledger Fabric，这是一种专门的区块链框架，旨在安全地管理联合学习环境中不同各方之间的数据和交易。它充当分布式分类帐，记录所有本地节点的更新，并有助于确保整个过程的透明度。这通过防止任何单点故障来增加安全性，如果只有一个系统控制整个过程，则可能发生。

Furthermore, the researchers propose the use of a token-based incentive system to enable fair data sharing and utilization. One major challenge encountered with the FL model is ensuring that participants are willing to share their data and that they follow the protocols. In some systems, participants could send heavily obscured, and therefore less helpful, data and still access the benefits of the learning model as others who contributed more meaningful learning experiences.

此外，研究人员建议使用基于令牌的激励系统来实现公平的数据共享和利用。 FL模型遇到的一个主要挑战是确保参与者愿意共享他们的数据并遵循协议。在某些系统中，参与者可能会发送大量遮盖的数据，因此数据的数据较小，并且仍然可以访问学习模型的好处，因为其他人贡献了更有意义的学习经验。

The SBTLF method tackles this problem by introducing token-based incentives that are linked to the privacy parameter. Participants earn tokens by sharing less obscured (less noise-added) data. Clearer (less obscured) data earns more tokens. These tokens can then be used to access global model updates and to reward participants who contribute more valuable data. Additionally, by encrypting the model updates and using blockchain for security, the risks associated with security attacks and a single point of failure are drastically reduced.

SBTLF方法通过引入与隐私参数相关的基于令牌的激励措施来解决此问题。参与者通过共享较不遮盖（噪音较少）的数据来赚取令牌。更清晰（不太模糊）的数据可获得更多的令牌。然后，这些令牌可用于访问全球模型更新，并奖励贡献更有价值数据的参与者。此外，通过加密模型更新并使用区块链进行安全性，与安全攻击和单个故障相关的风险大大减少了。

While this research opens up many exciting possibilities for secure collaborative machine learning, setting up a blockchain-based federated learning system can be technically complex and may require substantial computational resources. Moreover, introducing token-based incentives, which essentially involves a form of trading, may also require policy-level interventions.

尽管这项研究为安全的协作机器学习打开了许多令人兴奋的可能性，但建立基于区块链的联合学习系统在技术上可能很复杂，并且可能需要大量的计算资源。此外，引入基于令牌的激励措施本质上涉及一种交易形式，也可能需要政策级别的干预措施。

Nevertheless, the research provides a promising approach to making federated learning more secure and efficient. It introduces privacy tools and a clever reward system to ensure fair participation. By exploring ways to ease implementation and enhance resistance to different types of attacks, the research can ultimately pave the way for federated learning models to learn from decentralized data in a secure and effective manner. As the field continues to grow, integrating blockchain and privacy measures could set high standards for global data security and collaborative learning.

然而，这项研究提供了一种有希望的方法，使联盟学习更加安全有效。它引入了隐私工具和巧妙的奖励系统，以确保公平参与。通过探索缓解实施并增强对不同类型攻击的抵抗力的方法，该研究最终可以为联合学习模型以安全有效的方式从分散数据中学习。随着领域的不断增长，整合区块链和隐私措施可能会为全球数据安全和协作学习设定高标准。

This research news was partly generated using artificial intelligence and edited by an editor at Research Matters

该研究新闻部分使用人工智能生成，并由研究事项编辑编辑