在当今科技快速发展的背景下，大模型成为热议话题，人们普遍认为，通过扩大数据规模，可以无限提升模型的智能水平。这一观点在中国得到广泛认可，数据作为与土地、劳动力、资本、技术并列的第五大生产要素，其价值日益凸显。近年来，中国在数据要素市场化建设方面取得显著进展，旨在促进数据流通与复用，释放数据价值。
数据流通与复用的关键在于营造可信环境。与传统生产要素相比，数据具有独特的双面性，即业务价值越大，风险成本越高。因此，构建数据可信流通体系成为释放数据要素价值的底层支撑。在这一背景下，隐私计算技术逐渐成为学界和业界关注的焦点。
隐私计算技术，自概念诞生以来，历经近40年的发展，已从理论走向产业应用。然而，要使其成为数据流通市场的“基石技术”，还需克服一系列挑战。传统隐私计算主要侧重于多方合作场景下的计算安全，缺乏全面的安全视角，难以应对数据大规模流通带来的新风险，如运维者风险和加工者风险。此外，不同安全等级的数据需要采用不同的安全分级技术方案，以最大程度降低风险。
随着数据以密态形式流通成为趋势，传统隐私计算技术已无法满足新需求。为此，业界亟需创新，探索更适合未来数据流通场景的技术方案。未来，隐私计算技术的演进将更加注重整体安全视角的构建，以及针对不同数据安全等级的个性化解决方案。这一过程不仅将推动隐私计算技术的成熟，也将加速数据要素市场化进程，促进数字经济的健康发展。
总之，隐私计算技术从概念到产业应用的转变，不仅展示了技术的潜力，也为数据流通的未来描绘了一幅更加安全、高效、可信的蓝图。随着技术的不断演进，我们有理由期待一个数据价值得以充分释放，数字经济蓬勃发展的新时代。
英语如下：
News Title: “Privacy Computing: New Pillar for Data Circulation, Standards待Calling for Attention”
Keywords: Privacy Computing, Data Circulation, Technical Standards
News Content: Title: Privacy Computing: From Concept to Industrial Realization, Building a Trustworthy Data Circulation Environment
Against the backdrop of rapid technological advancements, the concept of large-scale models has become a hot topic. It is widely believed that by increasing data scale, one can infinitely enhance the intelligence level of models. This perspective is highly endorsed in China, where data is recognized as the fifth major production factor alongside land, labor, capital, and technology, its value is increasingly being highlighted. In recent years, China has made significant progress in the marketization of data elements, aiming to facilitate data circulation and reuse, and unlock data value.
The key to data circulation and reuse lies in establishing a trustworthy environment. Compared to traditional production factors, data has a unique dual nature: the greater the business value, the higher the risk cost. Therefore, constructing a trustworthy data circulation system has become the foundational support for unlocking the value of data elements. In this context, privacy computing technology has gradually become a focus of attention for academia and industry.
Privacy computing technology, since its conceptual inception, has evolved over nearly 40 years, transitioning from theoretical concepts to industrial applications. However, for it to serve as the “pillar technology” for data circulation markets, it must overcome a series of challenges. Traditional privacy computing primarily focuses on securing computations in multi-party collaboration scenarios, lacking a comprehensive security perspective, and is ill-equipped to address new risks posed by large-scale data circulation, such as operator risks and processor risks. Moreover, different security levels of data require distinct security classification techniques to minimize risks.
As data begins to circulate in encrypted form, traditional privacy computing technologies are no longer sufficient to meet new demands. The industry urgently needs innovation to explore more suitable technical solutions for future data circulation scenarios. In the future, the evolution of privacy computing technology will pay more attention to the construction of an overall security perspective and personalized solutions tailored to different data security levels. This process will not only drive the maturation of privacy computing technology but also accelerate the marketization of data elements, promoting the healthy development of the digital economy.
In summary, the transformation of privacy computing technology from concept to industrial application not only showcases the potential of the technology but also paints a more secure, efficient, and trustworthy future for data circulation. As technology continues to evolve, we have every reason to anticipate a new era where data value is fully realized, and the digital economy thrives.
【来源】https://www.jiqizhixin.com/articles/2024-07-10-12
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