国广清科：隐私计算与数据资产管理，在保护与利用中寻找平衡

在当今的数字化世界中，数据已成为关键的资产。随着技术的发展，我们正面临一个矛盾的现象：数据的重要性与日俱增，但与此同时，数据的获取、处理和利用方式也引发了大量的争议和担忧。尤其是在数据的隐私保护方面，社会各界都在呼吁更为严格和全面的解决方案。这其中，隐私计算技术的崛起为我们提供了一个可能的解决路径。

In today's digital world, data has become a critical asset. With the advancement of technology, we are facing a paradoxical phenomenon: the increasing importance of data, but at the same time, the methods of acquiring, processing, and utilizing data have also sparked a lot of controversy and concern. Especially in terms of data privacy protection, all sectors of society are calling for more rigorous and comprehensive solutions. Among them, the rise of privacy computing technology provides us with a possible path to resolve this issue.

隐私计算，也称为隐私保护计算或安全计算，是指在保证数据隐私的前提下，对数据进行处理和分析的技术集合。它融合了密码学、统计学、人工智能等多个学科的知识，旨在实现在不泄露数据原始内容的前提下，完成数据收集、存储、处理和分析的全过程。

Privacy computing, also known as privacy-preserving computing or secure computing, refers to a set of technologies that process and analyze data while ensuring data privacy. It combines knowledge from multiple disciplines such as cryptography, statistics, and artificial intelligence to achieve the entire process of data collection, storage, processing, and analysis without revealing the original content of the data.

在财政部2024年1月引发的《关于加强数据资产管理的指导意见》中明确提出，要加强数据全生命周期管理，并特别强调了数据安全和隐私保护的重要性。这为隐私计算在数据资产管理中的应用提供了明确的政策导向。通过隐私计算，组织可以在不泄露敏感信息的前提下，对数据进行有效的分析和利用，从而在保护个人隐私的同时，充分释放数据资产的价值。

The "Guiding Opinions on Strengthening the Management of Data Assets" issued by the Ministry of Finance in January 2024 clearly states that it is necessary to strengthen the management of the entire lifecycle of data and emphasizes the importance of data security and privacy protection. This provides a clear policy direction for the application of privacy computing in data asset management. Through privacy computing, organizations can effectively analyze and utilize data without disclosing sensitive information, thereby protecting individual privacy while fully unlocking the value of data assets.

然而，隐私计算并非万能的银弹。它面临着技术成熟度、成本、合规性等多方面的挑战。例如，目前大多数隐私计算方案都依赖于复杂的数学工具和较高的计算资源，这在一定程度上限制了其在大数据场景下的广泛应用。此外，由于隐私计算涉及到的技术门类众多，不同的方案可能存在兼容性问题，这无疑增加了其在实际应用中的复杂性。

However, privacy computing is not a panacea. It faces challenges in terms of technical maturity, cost, compliance, and other aspects. For example, most current privacy computing solutions rely on complex mathematical tools and high computational resources, which limits their widespread application in large-scale data scenarios. Additionally, due to the numerous technical categories involved in privacy computing, different solutions may have compatibility issues, which undoubtedly increases their complexity in practical applications.

再者，隐私计算也需要面对法规和政策环境的挑战。在全球范围内，虽然许多国家和地区都已经出台或正在制定相关的数据保护法规，但关于隐私计算的标准化和合规性问题仍然存在模糊地带。组织在使用隐私计算进行数据资产管理时，需要充分考虑其与现有法规的合规性，避免因操作不当而引发法律风险。

Moreover, privacy computing also needs to face challenges in regulatory and policy environments. Globally, although many countries and regions have already introduced or are in the process of formulating relevant data protection regulations, there are still gray areas regarding the standardization and compliance issues of privacy computing. When organizations use privacy computing for data asset management, they need to fully consider their compliance with existing regulations to avoid legal risks caused by improper operations.

尽管如此，我们仍然可以看到隐私计算在数据资产管理中的巨大潜力和价值。通过不断的技术创新和标准化工作，我们有理由相信，未来隐私计算将更好地服务于数据资产管理领域，帮助组织在保护个人隐私的同时，充分挖掘和释放数据资产的价值。

为了实现这一愿景，需要多方面的共同努力。首先，政府和相关监管机构应加强政策引导和立法工作，为隐私计算的发展和应用提供清晰的合规框架。其次，学术界和产业界应加强合作，推动隐私计算技术的研发和创新，降低其应用门槛和成本。此外，各类组织也应提高对隐私计算的认知度和重视程度，将其纳入数据资产管理的整体战略中。

同时，《关于加强数据资产管理的指导意见》也强调了人才培养的重要性。在隐私计算领域，我们需要培养一批既懂技术又懂法规的复合型人才，以应对日益复杂的隐私保护和数据资产管理挑战。通过加强教育和培训，我们可以为这一领域输送更多合格的人才，推动其持续健康发展。

At the same time, the Guiding Opinions on Strengthening Data Asset Management also emphasize the importance of talent cultivation. In the field of privacy computing, we need to cultivate a group of compound talents who understand both technology and regulations to cope with increasingly complex privacy protection and data asset management challenges. Through strengthening education and training, we can provide more qualified talents for this field and promote its sustainable and healthy development.

作为行业的积极推动者，国广清科将继续积极向全行业提供隐私计算全栈技术服务。利用隐私计算“可用不可见”的技术特性，不断探索隐私计算前沿技术在商业应用中的落地。我们将为行业的发展添砖加瓦，持续创新，促进数字经济高质量发展。同时，我们将推动数据要素流通关键基础技术发展，助力构建更加安全、可靠的数据应用环境。

As an active promoter in this industry, CRI TSING'STECH will continue to actively provide full stack technical services for privacy computing across the industry. Leveraging the technical characteristics of "usable but unseen" privacy computing, we continuously explore the implementation of cutting-edge privacy computing technologies in commercial applications. We will contribute to the development of the industry, promote continuous innovation, and facilitate high-quality economic development in the digital economy. At the same time, we will promote the development of key enabling technologies for data element circulation to help build a safer and more reliable data application environment.