国广清科：人工智能的“奇点”，隐私计算的“原爆点”

隐私计算是在保护数据本身不对外泄露的前提下实现数据分析计算的技术集合，达到对数据“可用、不可见”的目的；在充分保护数据和隐私安全的前提下，实现数据价值的转化和释放。基于这一特性，隐私计算可以确保在不泄露数据内容的情况下，帮助人工智能获取更多、更广、更深的数据资源。

Privacy computing is a technology collection that realizes data analysis and computing on the premise of protecting the data itself from being leaked to the outside world, so as to achieve the purpose of "usable and invisible" data; on the premise of fully protecting data and privacy security, it realizes the conversion and freed of data value. Based on this feature, privacy computing can help artificial intelligence obtain more, wider, and deeper data resources without revealing data content.

众所周知，人工智能的三大基础是算法、数据、算力。在数据层面，如何在合规尊重用户隐私的条件下充分发挥数据价值，如何打破壁垒、连接数据孤岛、提高可用数据的数量和质量，已成为人工智能发展需要解决的关键问题，而隐私计恰恰就是打破数据孤岛、拓展数据疆界的关键技术。

As we all know, the three foundations of artificial intelligence are algorithms, data, and computing power. At the data level, how to give full play to the value of data under the condition of compliance and respect for user privacy, how to break down barriers, connect data islands, and improve the quantity and quality of available data have become key issues to be solved in the development of artificial intelligence. It is the key technology to break data silos and expand data boundaries.

这意味着，人工智能技术革命背后意味着巨大的隐私挑战。在以深度学习为主导的人工智能浪潮中，基于大数据的深度学习技术最开始在互联网领域成功落地，并广泛运用于搜索、推荐、语音识别、机器翻译等各个方面。随着人工智能超级模型工作的推进，算法训练模型所使用的数据规模越大，模型参数规模越大，模型在使用时候识别精度就越高。

This means that there are huge privacy challenges behind the technological revolution of artificial intelligence. In the wave of artificial intelligence dominated by deep learning, deep learning technology based on big data first successfully landed in the Internet field, and is widely used in various aspects such as search, recommendation, speech recognition, and machine translation. With the advancement of artificial intelligence super model work, the larger the data scale used by the algorithm training model, the larger the model parameter scale, and the higher the recognition accuracy of the model when it is used.

在技术层面，隐私计算不同技术路线正在走向融合。与此同时，隐私计算与区块链、人工智能、工业互联网也逐步融合，尤其是隐私计算与硬件的结合已产生多款一体机等软硬件结合产品，预计将会越来越丰富。隐私计算已成为AIGC中隐私保护的重要工具。在AIGC服务的生命周期中，用于训练的大规模数据集和用户的私人信息需要得到保护，可以利用联邦学习来解决训练数据的安全问题。

At the technical level, different technical routes of privacy computing are moving toward integration. At the same time, privacy computing is gradually integrating with blockchain, artificial intelligence, and the industrial Internet. In particular, the combination of privacy computing and hardware has produced a variety of all-in-one computers and other hardware-software products, which are expected to become more and more abundant. Privacy computing has become an important tool for privacy protection in AIGC. In the life cycle of AIGC services, large-scale datasets used for training and private information of users need to be protected, and federated learning can be used to solve the security problem of training data.

在此基础上，将可信执行环境（TEE）和联邦学习技术结合，可以为大语言模型提供更完善的隐私和数据保护。借助基于TEE的联邦学习解决方案，在训练阶段，TEE中的数据处理都处于加密状态，在推理阶段TEE能够保护用户输入和模型结果的隐私；同时，TEE的硬件隔离和安全验证机制可以防止未经授权的访问和攻击，增强模型运行时的安全性。

On this basis, the combination of trusted execution environment (TEE) and federated learning technology can provide better privacy and data protection for large language models. With the TEE-based federated learning solution, during the training phase, the data processing in the TEE is encrypted, and during the inference phase, the TEE can protect the privacy of user input and model results; at the same time, TEE's hardware isolation and security verification mechanism can prevent unauthorized access and attacks, and enhance the security of the model runtime.

总而言之，以AIGC的“奇点”为契机，隐私计算有望迎来“原爆点。联邦学习、TEE等多种技术体系间也将实现更深刻的融合。

All in all, taking AIGC's "singularity" as an opportunity, privacy computing is expected to usher in the "ground zero point". Various technical systems such as federated learning and TEE will also achieve deeper integration.