AI 中文总结
研究针对共享数据库和数据流中隐私泄露问题，开发pArborist工具，结合数据模式与生产者输入，半自动创建查询识别标记隐私泄露事件，实验显示其在召回率、精确率及检测能力上表现出色，远超现有方法。
AI 中文摘要
共享数据库和数据流存在以复杂事件形式泄露私人信息的风险，识别此类隐私泄露复杂事件对维护隐私和数据效用至关重要。但数据生产者往往缺乏专业知识来全面识别这些事件，影响了许多依赖准确事件标记的隐私保护机制。为此开发了pArborist工具，它能根据数据生产者的隐私要求，在静态数据集和动态数据流中半自动创建一组查询来识别和标记隐私泄露复杂事件。该工具结合数据库或数据流模式及数据生产者的初始输入（种子查询），通过计算资源上限约束生成包含所有可能语法正确查询的树，并进行优化。评估表明，pArborist在查找隐私泄露查询方面召回率达90%，精确率达93%，远超FQID。在数据流处理实验中，平均预热920毫秒后延迟约1.3毫秒，还能根据GDPR自动检测隐私泄露复杂事件。
英文摘要
Sharing databases and data streams imposes the danger of revealing private information in the form of complex events which can comprise individual data elements and their combinations. Identifying these privacy-revealing complex events is crucial for preserving privacy while maintaining data utility. However, data producers often lack the expertise to comprehensively identify these events, which undermines many state-of-the-art privacy-preserving mechanisms that rely on accurate event labeling. To address this challenge, we developed pArborist - a tool that can semi-automatically create a set of queries to identify and label privacy-revealing complex events in both static datasets and dynamic data streams, guided by the privacy requirements of the data producer. pArborist uses the schema of the database or data stream combined with initial input from the data producer, i.e., seed queries. From each seed query, pArborist grows a tree containing all possible syntactically correct queries, constrained by an upper limit on computational resources. Following this growing phase, the tree is refined by eliminating queries that lack correlation to the seed or are conditionally independent of the seed. Our evaluation indicates that pArborist achieves overall recall of 90% and precision of 93% in finding privacy-revealing queries, and this significantly surpasses the state-of-the-art approach FQID. In data stream processing experiments, pArborist introduces a delay of approximately 1.3 ms following an average warm-up period of 920 ms. The experiments also show that pArborist can automatically detect privacy-revealing complex events according to GDPR.
Journal refHe Gu, Thomas Plagemann, and Vera Goebel (2026) Article 1, 1-12
