Inference-Aware & Privacy-Preserving Deletion in Databases

2026-03-31

AI Summary

Traditional database deletion mechanisms often fail to prevent privacy leakage, as deleted content may still be inferred through data dependencies, derived data, or the act of deletion itself. This work proposes a novel inference-centric perspective on data deletion, distinguishing among logical, physical, and semantic deletion. It systematically analyzes two primary channels of information leakage—post-deletion state and deletion patterns—and formally characterizes the inferential boundaries that persist after deletion. Building on this analysis, the study develops a design framework for deletion mechanisms that provide semantic privacy guarantees, clarifies key challenges, and delineates the associated design space. By doing so, it establishes a theoretical foundation and technical roadmap for achieving truly privacy-preserving deletion, advancing database privacy mechanisms toward semantic security.

Abstract

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.

Problem

• privacy-preserving deletion
• inference leakage
• database deletion
• semantic deletion
• deletion pattern

Innovation

• inference-aware deletion
• privacy-preserving deletion
• semantic deletion
• deletion leakage
• database privacy