Group 1 - The core viewpoint of the article is that the privacy computing market in China is projected to reach a scale of 980 million yuan in 2024, reflecting a year-on-year growth of 10.1% [1] - Ant Group's subsidiary, Ant Financial Technology, holds the leading market share in China's privacy computing sector with a 36.7% share, marking the third consecutive year it has maintained this position [1]

Company Dynamics - The "Trustworthy Privacy Computing Open Source Community" announced an upgrade on its third anniversary, expanding to "Yin Yu·Data Trustworthy Circulation Technology Community," covering six major technology routes including privacy protection computing and blockchain [2] - The upgrade aims to provide a one-stop, verifiable, and interconnected data circulation technology foundation for various sectors such as healthcare, finance, urban governance, and manufacturing [2][3] - The community faces challenges in industrial application, including difficulties in consensus, standardization, and high application thresholds, which hinder the scaling of data circulation [2] Collaborative Efforts - Ant Group, along with various academic and research institutions, initiated the upgrade to build a technology ecosystem that integrates trustworthy data space, data components, and blockchain [3] - The upgraded community will aggregate full-stack data circulation technology, offering higher computing performance and addressing industry challenges such as data theft during operations [3] Achievements and Future Plans - Since July 2022, Ant Group has open-sourced its core privacy computing technologies, connecting over 20,000 developers and more than 60 industry partners [4] - The community has achieved significant results in areas like inclusive finance and public data development, supporting projects such as the Hangzhou Privacy Computing Center [4] - Ant Group has invested in privacy computing technology since 2016, holding over 1,700 patents and leading or participating in more than 80 domestic and international standards [4]

新浪科技讯 8月14日下午消息，今日，隐语"可信隐私计算开源社区"在三周年之际宣布升级：由"可信 隐私计算开源社区"扩容为"隐语·数据可信流通技术社区"，将逐步覆盖隐私保护计算、可信数据空间、 数据元件、数联网、区块链、数场等六大技术路线并形成多技术路线的融合，未来通过开源代码、共建 标准和共创场景，为医疗、金融、城市治理、制造等多个领域提供一站式、可验证、可互联的数据流通 技术底座。 在国家"数据要素×"行动全面提速的背景下，数据可信流通已从顶层设计走向标杆落地，但在产业应用 上仍陷"三重门"困境：共识难，有不少成功案例落地，但技术实现不统一、不透明，规模化流通落地卡 在"最后一公里"；标准难，相关文件和标准修订周期长、颗粒度粗，难以直接映射到技术的实现场景 上；应用门槛高，技术难以转化为易用、易复制的生产工具，产业应用还需要一定的周期。 自2022年7月起，蚂蚁集团可信隐私计算的核心技术已经陆续全部开源。目前，"隐语"社区链接全球超 20，000名开发者、70余所高校及科研机构、60余家产业合作伙伴。在产业落地上，隐语在普惠金融、 新能源车险等领域取得显著成效。在公共数据开发方面，隐语支持杭州市密态计算中 ...

（责任编辑：华青剑） 与此同时，新网银行策划主题直播，两位AI专家在直播间细致讲解科技实践应用，生动展示 AIGC、大语言模型等前沿技术，多维度呈现人工智能带来的科技成果，营造热爱科学、崇尚创新的浓 厚氛围，并提高了公众对金融科技的认识和兴趣。直播中，嘉宾们还结合当前网络安全热点话题，提醒 观众在体验AI技术便捷性的同时，也要警惕各类"AI投毒""AI幻觉"。新网银行视频号、微博号、抖音号 多平台现场直播，累计观看人数实现10万+。 新网银行深化数字化战略布局，依靠自身力量，深度融合大数据、隐私计算与人工智能等数字技 术，构建起贯通多场景的开放生态平台，形成了全在线、全实时、全客群的银行业务模式。面向未来， 新网银行将深化前沿技术与金融业务场景的融合创新，通过打造多元化数字普惠金融产品，满足大众多 层次金融需求，以数字技术培育新质生产力，扎实做好五篇大文章的时代答卷。 近期，新网银行以全国金融科技活动周为契机，围绕"矢志创新发展，建设科技强国"主题，精心策 划并开展了一系列丰富多彩的金融科技宣传活动，积极面向公众宣传科普各类知识，为建设科技强国贡 献金融力量。 在全国金融科技活动周期间，新网银行充分利用线上渠 ...

Core Viewpoint - The report emphasizes that the market-oriented allocation of data elements is crucial for promoting high-quality development of the digital economy, fostering new industries, models, and productive forces [1] Data Element Market Development - Since 2024, significant breakthroughs have been made in the development and utilization of data elements in China, accelerating the construction of the data element market and enhancing its role in economic development [1] - The scale of data transactions has expanded significantly, with data exchanges optimizing their functions and diversifying transaction categories, including financial, medical, and industrial data [1][2] Data Ecosystem and Market Size - The number of data enterprises in China has grown from 110,000 to over 1,000,000 in the past decade, playing a key role in revitalizing data resources [2] - The data element market is expected to continue expanding, becoming a key driver of economic growth and facilitating higher quality development [2] Recommendations for Market Reform - The report suggests accelerating the improvement of data property rights systems, establishing a unified regulatory framework for data transactions, and innovating fiscal and tax incentive mechanisms to promote the development and utilization of data elements [2][3] Infrastructure and Technology Innovation - There is a push to enhance high-performance computing centers and intelligent computing centers, as well as to develop a robust data transmission network to support the efficient operation of the data element market [3] - Encouragement for enterprises and research institutions to invest in key technologies such as privacy computing, blockchain, and artificial intelligence to improve data security and circulation efficiency [3] Market Participant Engagement - The report highlights the importance of activating various data holders, including government departments, enterprises, and research institutions, to participate in market transactions and data asset activities [3] - Support for the development of professional data operators and trusted data service providers is emphasized to create a comprehensive ecosystem for data supply, rights confirmation, pricing, and compliance [3] Data Security and Regulation - Establishing a robust data security assurance system and enhancing the management of data throughout its lifecycle is crucial for maintaining market order and protecting data security [3]

Core Insights - The collaboration between Zhongke Shuguang and China Mobile aims to address the inefficiencies in data storage and computing power allocation in the "East Data West Computing" initiative [1][2] - The current investment ratio in computing power versus storage in China is approximately 40:1, compared to around 10:1 in foreign markets, indicating a significant imbalance [1][2] - Shuguang Storage has developed a unified scheduling platform to enhance data flow and management across different data centers, addressing key challenges in data visibility, organization, and utilization [3][4][5] Group 1: Industry Challenges - The primary bottleneck in AI infrastructure is not computing power but rather the foundational data layer, which is often fragmented and incompatible across different systems [3][4] - Data centers face issues such as lack of visibility into data assets, inefficient data flow, and high latency in accessing stored data, particularly in the context of the "East Data West Computing" strategy [4][5] - The rapid growth of AI applications has increased the performance requirements for data storage systems, necessitating advancements in IOPS and bandwidth capabilities [6][12] Group 2: Technological Advancements - Shuguang Storage has transitioned from being a follower to a solution provider in the storage technology space, leveraging over 21 years of R&D to establish a competitive edge [2][10] - The company has achieved global recognition for its centralized storage products, ranking first in the SPC-1 international benchmark for storage performance [2][10] - The development of a unified storage scheduling platform is seen as essential for facilitating data flow across heterogeneous and geographically dispersed data centers [7][8] Group 3: Strategic Collaborations - The partnership with China Mobile was initiated to address specific business needs and to support the national strategy of optimizing resource utilization in the West [7][8] - Shuguang Storage's extensive self-research capabilities position it as a key player in solving complex data flow challenges, which many other vendors struggle to address [8][9] Group 4: Future Outlook - The entry barriers for AI applications are expected to lower with advancements in technology, but new challenges related to privacy and data security will emerge [12][13] - Enhancing GPU resource utilization is critical for reducing AI operational costs, with Shuguang Storage's innovations in data transfer efficiency playing a pivotal role [13][14] - The company's focus on "storage-computing synergy" aims to optimize the interaction between storage systems and GPU resources, significantly improving processing speeds and efficiency [14]

Group 1 - The National Healthcare Security Administration (NHSA) is promoting the application of artificial intelligence and big data in the healthcare insurance sector, transitioning from traditional methods to intelligent and precise fund supervision [1][2] - The healthcare insurance data encompasses 1.33 billion insured individuals, 50 million employers, 1.14 million hospitals and pharmacies, 17,900 medical supply companies, and 376,000 medical products, supporting an annual revenue and expenditure of approximately 3 trillion yuan and 10 billion medical service transactions [1] - The 2025 National Smart Healthcare Insurance Competition will adopt an open model without specific tracks, focusing on various fields such as healthcare, innovative drug development, financial insurance, and academic research [2] Group 2 - Participants in the competition will have access to real patient data regarding medical treatment, settlement, fund operation, and drug usage, which will enhance the feasibility and practicality of their proposals [2] - The competition marks the first attempt to integrate healthcare insurance data with data from other industries, facilitating interconnectivity and collaboration among healthcare institutions, research institutes, universities, and corporate R&D centers [2] - The Shanghai government will provide a secure and compliant local healthcare insurance data verification environment and computational resources for projects aiming for local implementation [3]

Group 1 - Zhejiang University Netnew Technology Co., Ltd. announced that its wholly-owned subsidiary won a bid for the intelligent engineering project of the Zhejiang Provincial Public Health Clinical Center, with a contract value of 94.2712 million yuan [1] - The project includes multiple subsystems such as comprehensive wiring, computer networks, security monitoring, energy management, and information release, indicating a high level of integration and extensive coverage [1] - The project is seen as a significant step in the company's transformation towards "infrastructure digitization," showcasing a closed-loop practice of "computing power - model - data" [1] Group 2 - The company is transitioning from a traditional system integrator to a digital platform enterprise, focusing on integrating cutting-edge technologies such as AI, privacy computing, blockchain, and big data [2] - The company has established a comprehensive AI model service system, integrating computing power resources and providing solutions for computing and network infrastructure [2] - The core competitiveness of building a digital platform requires investment in key technologies and addressing challenges related to computing power deployment and user acceptance [2] Group 3 - The company is exploring the development of a large model platform, launching the "Big Teacher Model" development platform based on its computing resources [3] - The "Zhejiang Teacher" intelligent platform for universities has been officially launched, enhancing the platform's capabilities and establishing a foundation for nationwide promotion in the education sector [3] - The company is positioned to become a provincial-level computing power scheduling platform operator, with potential applications in urban data asset management and public service intelligence [3]

Core Viewpoint - The article emphasizes the upcoming opportunities and challenges in the Chinese fintech industry as it transitions into a new phase of digital finance and technology scene construction, driven by advancements in generative AI, blockchain, and other cutting-edge technologies [1][3]. Group 1: Research Background - 2026 marks the beginning of a new round of the "Financial Technology Development Plan," focusing on the integration of AI and stablecoin technologies to enhance cross-border payment processes and develop financial scenarios around data value [1]. - The report aims to analyze the practical needs of financial institutions regarding advanced technologies and digital financial practices, providing guidance for technology vendors [1][3]. Group 2: Purpose of the Report - The report aims to help industries and capital track the latest practices in China's fintech sector and identify future market opportunities, with a planned release in January 2026 [2]. - The report will invite participation from financial institutions and fintech service providers to explore market trends and technology needs [2]. Group 3: Research Content - The report will focus on the latest iterations of technologies like generative AI and blockchain, analyzing their impact on the fintech industry and identifying key trends for development [3][4]. - It will examine five core financial scenarios: technology finance, green finance, inclusive finance, pension finance, and digital finance, assessing the empowering effects of technological iterations on these areas [3][4]. Group 4: Participation Value - Participating companies will have the opportunity to be featured in the report, enhancing their brand visibility and industry influence [6]. - The report will be disseminated through official platforms and media channels, providing extensive exposure [6]. Group 5: Target Enterprises - The report targets financial industry clients, including banks, insurance, securities, and fintech service providers that have engaged in fintech practices [9]. - It also includes technology service providers, both listed and unlisted, that offer fintech products or services [9]. Group 6: Timeline for Participation - The call for participation is open until December 15, 2025, inviting financial institutions and fintech service providers to engage [10].

Core Viewpoint - The rapid development of data assetization is driven by the integration of the Internet of Things (IoT) and Real Data Assets (RDA), which is reshaping the industrial logic of data assetization [1][2][5]. Group 1: Data Assetization and RDA - RDA (Real Data Assets) extends the concept of RWA (Real World Assets) by focusing on the authenticity verification and value enhancement of data, encapsulating operational data from physical assets to improve credit, transparency, and regulatory compliance [5][6]. - The true breakthrough of RDA is fueled by the real-time data continuously released by IoT devices, which serve as a natural "data mine" and "value factory" for RDA [7][8]. - RDA aims to complete the entire process of data ownership, value realization, and financialization, requiring data assets to have clear ownership, traceable sources, and continuous flow to participate in capital market transactions [8][9]. Group 2: Role of IoT in RDA - IoT enables real-time data collection from various physical assets, providing high credibility, strong relevance, and traceable foundational data for RDA, facilitating the transformation of data from a "resource" to an "asset" [7][8]. - The collaboration between IoT and RDA will gradually reveal its synergistic effects in specific application scenarios, such as dynamic transparency in battery lifecycle management and real-time operational data in offshore wind power maintenance [9][10]. Group 3: Financial Implications and Innovations - The integration of IoT, stablecoins, and RDA/RWA creates a closed-loop for machine economy, allowing devices to autonomously participate in value flow and enabling real-time settlement without traditional financial intermediaries [13][14]. - The emergence of stablecoins as a financial infrastructure will significantly enhance autonomous trading and automatic settlement among devices, improving industrial collaboration efficiency [14][15]. - The dynamic credit era will see IoT devices transition from passive data contributors to active data asset owners, leading to a transformation in asset valuation and credit assessment [16][17]. Group 4: Challenges and Future Outlook - The distributed nature of data asset circulation and pricing may introduce new risk management challenges, such as data falsification, necessitating the implementation of hardware trust mechanisms to ensure data authenticity [19][20]. - The evolution of data assetization and pricing power presents both challenges and opportunities for IoT companies, potentially allowing them to transition from "data producers" to "data bankers" [20][21].