透过一审判决，我们不难看出蕴含在个案里的这项重要原则：用户驾驶行为产生的数据，虽由车企记录 和存储，但其关涉用户人身财产安全时，消费者有权获取，经营者有义务提供。 跳出个案看争议，本案的焦点直指数字社会中一个普遍而紧迫的新问题：因用户行为产生却存储于厂商 服务器的数据，权属究竟归谁？是企业可视为己有的"数据资产"，还是用户享有访问、使用和受益 的"个人数据"？目前我国法律尚未对智能设备生成数据的权属作出明确规定，但民法典、个人信息保护 法等已为个人对信息享有权益设定了基石。即便数据权属存在讨论空间，用户的"数据知情权"和"使用 权"也应受到优先保护——这不仅关乎公平，更关乎安全。 车主的知情权，在此类事件中应被视为一项基本的衍生权利。驾驶行为数据尽管经厂商设备采集，但其 描述的是用户自身的行车状态，关联着生命财产安全这一最高阶的法治利益。车企以"法律未明确规 定"为由拒绝提供关键数据，实质上构成了对消费者权益的架空。法院此次判决，正是对这种新型"数据 不对称"的一次司法矫正，也为未来类似纠纷提供了裁判范式。 9月16日，北京市大兴区人民法院就张女士诉特斯拉汽车销售服务（北京）有限公司等买卖合同纠纷一 案作出判 ...

Core Viewpoint - The ownership of patient medical records is a contentious issue, with hospitals, doctors, and patients each claiming rights over the data generated during medical treatment [1][2]. Group 1: Data Ownership and Privacy - Data is recognized as a new production factor, but its ownership remains disputed, particularly regarding patient medical records [1]. - Patients consider their medical records as personal privacy, while doctors argue that their expertise is necessary for data generation, and hospitals claim that without their equipment, data cannot exist [1]. - Ordinary outpatient medical records are typically owned or managed by patients, while inpatient records are managed or owned by hospitals [1]. Group 2: Challenges in Data Utilization - The complexity of data ownership leads to difficulties in data circulation and utilization, with concerns about data leakage and privacy infringement [2]. - The concept of "privacy computing" is proposed as a potential solution, allowing data value extraction without accessing original data, thus addressing ownership ambiguities [2]. - Privacy computing enables collaborative data use without transferring data outside its original domain, mitigating security and privacy risks [2]. Group 3: Technical Aspects of Privacy Computing - Privacy computing faces performance limitations, particularly in distributed models that rely on complex algorithms and frequent data transmission [3]. - New centralized privacy computing models have emerged to alleviate performance issues by encrypting data within a trusted execution environment [3]. - A hybrid approach combining centralized and distributed privacy computing is recommended based on specific needs, balancing data security and performance [3].