Tee-based key-value stores: a survey

2024-12-18

The VLDB journal

AI Summary

Trusted Execution Environments (TEEs) face inherent trade-offs among security guarantees, memory constraints, and performance bottlenecks in key-value (KV) storage systems. Method: This paper presents the first comprehensive taxonomy and cross-cutting comparative analysis of TEE-based KV systems, establishing a unified evaluation framework grounded in TEE security principles, distributed systems theory, and database fundamentals. Leveraging empirical studies on Intel SGX and ARM TrustZone, we systematically classify and analyze 12 representative designs. Contribution/Results: We identify fundamental bottlenecks—including remote attestation overhead, encrypted I/O latency, and state synchronization complexity—and articulate optimization pathways. Our analysis formalizes the “security–efficiency–functionality” triadic trade-off, providing both theoretical foundations and practical design guidelines for secure KV storage. The work advances the deployment of TEEs in trustworthy data management by bridging system design with real-world platform constraints.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Problem

• Data Security
• Trusted Execution Environment
• Memory and Performance Limitations

Innovation

• Trusted Execution Environment
• Data Security
• Performance Optimization