Inference-Aware & Privacy-Preserving Deletion in Databases
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910091508187136 |
|---|---|
| author | Chakraborty, Vishal Kaminsky, Youri Dhariya, Arnav Abhijit Mehrotra, Sharad Naumann, Felix Pandey, Sarvesh |
| author_facet | Chakraborty, Vishal Kaminsky, Youri Dhariya, Arnav Abhijit Mehrotra, Sharad Naumann, Felix Pandey, Sarvesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00326 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inference-Aware & Privacy-Preserving Deletion in Databases Chakraborty, Vishal Kaminsky, Youri Dhariya, Arnav Abhijit Mehrotra, Sharad Naumann, Felix Pandey, Sarvesh Databases 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. |
| title | Inference-Aware & Privacy-Preserving Deletion in Databases |
| topic | Databases |
| url | https://arxiv.org/abs/2604.00326 |