Adaptive Privacy-Preserving SSD
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912443750416384 |
|---|---|
| author | Ahn, Na Young Lee, Dong Hoon |
| author_facet | Ahn, Na Young Lee, Dong Hoon |
| contents | Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels (PL0-PL3) that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning adjusts levels contextually, boosting privacy with negligible performance overhead and complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Privacy-Preserving SSD Ahn, Na Young Lee, Dong Hoon Cryptography and Security H.3 Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels (PL0-PL3) that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning adjusts levels contextually, boosting privacy with negligible performance overhead and complexity. |
| title | Adaptive Privacy-Preserving SSD |
| topic | Cryptography and Security H.3 |
| url | https://arxiv.org/abs/2506.02030 |