Deletion is a fundamental database operation, yet modern systems often fail to provide the privacy guarantee that users expect from it. A deleted value may disappear from query results and even from physical storage, yet remain inferable from dependencies, derived data, or traces exposed by the deletion event itself. Meaningful deletion, therefore, requires more than logical removal or physical erasure; it requires a privacy guarantee that limits what remains inferable after deletion. In this paper, we take an inference-centric view of deletion, focusing on two leakage channels: leakage from the post-deletion state and leakage from the deletion pattern itself. We use this lens to distinguish logical, physical, and semantic deletion, organize the design space of deletion operations, and highlight open research challenges for building deletion mechanisms with meaningful privacy guarantees in database systems.

翻译：删除是数据库的基本操作，然而现代系统通常无法提供用户期望的隐私保障。被删除的值可能从查询结果甚至物理存储中消失，但仍可能通过依赖关系、派生数据或删除事件本身暴露的痕迹被推断出来。因此，有意义的删除不仅需要逻辑移除或物理擦除，更需要一种隐私保障机制，限制删除后仍可推断的信息。本文从推理为中心的视角审视删除操作，聚焦两种泄漏渠道：删除后状态的泄漏与删除模式本身的泄漏。基于此视角，我们区分了逻辑删除、物理删除和语义删除，梳理了删除操作的设计空间，并揭示了在数据库系统中构建具有有意义隐私保障的删除机制所面临的开放研究挑战。