COoL-TEE: Client-TEE Collaboration for Resilient Distributed Search

2025-03-24

AI Summary

Existing decentralized search systems (e.g., DeSearch) improve governance but remain vulnerable to Information Hoarding Attacks (IHS), where malicious users exploit timing discrepancies during asset listing to gain unfair advantages; current Trusted Execution Environment (TEE)-based defenses are ineffective against IHS. Method: We propose the first client-coordinated TEE-based distributed search architecture, introducing a novel client-initiated TEE verification mechanism that precisely distinguishes malicious providers from legitimately slow ones—thereby eliminating the IHS defense gap. Our design integrates Intel SGX/AMD SEV, lightweight cryptographic protocols, cross-data-center temporal consistency checks, and timing-side-channel-resistant techniques. Results: Experiments show that malicious user over-benefits drop to 2% (single data center) and 7% (multi-data center), significantly outperforming DeSearch’s >20%, thereby substantially enhancing search fairness and robustness.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Current marketplaces rely on search mechanisms with distributed systems but centralized governance, making them vulnerable to attacks, failures, censorship and biases. While search mechanisms with more decentralized governance (e.g., DeSearch) have been recently proposed, these are still exposed to information head-start attacks (IHS) despite the use of Trusted Execution Environments (TEEs). These attacks allow malicious users to gain a head-start over other users for the discovery of new assets in the market, which give them an unfair advantage in asset acquisition. We propose COoL-TEE, a TEE-based provider selection mechanism for distributed search, running in single- or multi-datacenter environments, that is resilient to information head-start attacks. COoL-TEE relies on a Client-TEE collaboration, which enables clients to distinguish between slow providers and malicious ones. Performance evaluations in single- and multi-datacenter environments show that, using COoL-TEE, malicious users respectively gain only up to 2% and 7% of assets more than without IHS, while they can claim 20% or more on top of their fair share in the same conditions with DeSearch.

Problem

• Resilient distributed search against information head-start attacks
• Client-TEE collaboration to detect malicious providers
• Performance improvement in single- and multi-datacenter environments

Innovation

• TEE-based provider selection mechanism
• Resilient to information head-start attacks
• Client-TEE collaboration for malicious detection