Hawk：云原生系统中由 DevOps 驱动的透明度与问责制
Hawk: DevOps-driven Transparency and Accountability in Cloud Native Systems
AI总结：
本文提出 Hawk，面向 DevOps 的发布、运行和监控阶段构建可集成于云原生系统的透明度信息收集与维护方法，并通过原型和实验验证其可行性与低开销。
摘要：
透明度是 GDPR 或 CCPA 等现代隐私法规最重要的原则之一。为了遵守此类监管框架，数据控制者必须向数据主体提供关于个人数据收集、处理、存储和传输的准确信息。为此，必须整理相应的事实和细节，并始终保持其最新状态。在传统的、相对静态的系统环境中，这种清单编制（包括处理目的或每个系统组件的存储期限等细节）可以手动完成。然而，在当前敏捷、由 DevOps 驱动且云原生的信息系统工程环境下，此类手动做法已不再适用，使数据控制者越来越难以实现监管合规。为了妥善收集和维护始终最新的透明度信息，并将其顺畅集成到 DevOps 实践中，我们在此提出一组新颖方法，这些方法明确针对 DevOps 生命周期中与运行时隐私相关透明度和问责事项最相关的特定阶段：发布（Release）、运行（Operation）和监控（Monitoring）。对于上述每个阶段，我们研究了在确定个人数据处理细节时出现的具体挑战，开发了不同的方法，并提供了相应的概念验证实现，这些实现可轻松应用于云原生系统。我们还展示了这些组件如何相互集成，以建立包含设计期和运行期元素的透明度信息。此外，我们的实验评估表明所产生的开销合理。在此基础上，数据控制者能够按照实际工程实践履行其监管透明度义务。
英文摘要：
Transparency is one of the most important principles of modern privacy regulations, such as the GDPR or CCPA. To be compliant with such regulatory frameworks, data controllers must provide data subjects with precise information about the collection, processing, storage, and transfer of personal data. To do so, respective facts and details must be compiled and always kept up to date. In traditional, rather static system environments, this inventory (including details such as the purposes of processing or the storage duration for each system component) could be done manually. In current circumstances of agile, DevOps-driven, and cloud-native information systems engineering, however, such manual practices do not suit anymore, making it increasingly hard for data controllers to achieve regulatory compliance. To allow for proper collection and maintenance of always up-to-date transparency information smoothly integrating into DevOps practices, we herein propose a set of novel approaches explicitly tailored to specific phases of the DevOps lifecycle most relevant in matters of privacy-related transparency and accountability at runtime: Release, Operation, and Monitoring. For each of these phases, we examine the specific challenges arising in determining the details of personal data processing, develop a distinct approach and provide respective proof of concept implementations that can easily be applied in cloud native systems. We also demonstrate how these components can be integrated with each other to establish transparency information comprising design- and runtime-elements. Furthermore, our experimental evaluation indicates reasonable overheads. On this basis, data controllers can fulfill their regulatory transparency obligations in line with actual engineering practices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
