Adaptive Privacy-Preserving SSD

TL;DR

This paper introduces an adaptive architecture for IoT SSDs that dynamically balances privacy, performance, and cost using machine learning to select appropriate data deletion techniques across four privacy levels.

Contribution

It presents a novel adaptive architecture with four privacy levels that intelligently selects deletion techniques, optimizing privacy and performance in IoT SSDs.

Findings

Effective privacy levels with minimal performance impact

Machine learning enables context-aware privacy management

Quantitative analysis shows balanced trade-offs

Abstract

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.