Leaking Queries On Secure Stream Processing Systems

2025-10-14

AI Summary

This paper identifies a novel timing side-channel threat against query logic in Intel SGX-based cloud stream processing systems: malicious cloud providers can infer sensitive query structures by analyzing execution-time characteristics—a risk overlooked by prior work, which focuses solely on data confidentiality. Method: We establish the core thesis that query logic is as sensitive as data itself, and propose a two-stage timing analysis attack model: (1) offline behavioral modeling of operators using synthetic data, and (2) online execution monitoring combined with machine learning to identify and reconstruct end-to-end query structures. Contribution/Results: Our attack achieves up to 92% success rate on SecureStream and NEXMark benchmarks. We further design a low-overhead defense mechanism that significantly enhances the logical security of SGX-accelerated stream processing systems without compromising performance.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Logic Programming

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Stream processing systems are important in modern applications in which data arrive continuously and need to be processed in real time. Because of their resource and scalability requirements, many of these systems run on the cloud, which is considered untrusted. Existing works on securing databases on the cloud focus on protecting the data, and most systems leverage trusted hardware for high performance. However, in stream processing systems, queries are as sensitive as the data because they contain the application logics. We demonstrate that it is practical to extract the queries from stream processing systems that use Intel SGX for securing the execution engine. The attack performed by a malicious cloud provider is based on timing side channels, and it works in two phases. In the offline phase, the attacker profiles the execution time of individual stream operators, based on synthetic data. This phase outputs a model that identifies individual stream operators. In the online phase, the attacker isolates the operators that make up the query, monitors its execution, and recovers the operators using the model in the previous phase. We implement the attack based on popular data stream benchmarks using SecureStream and NEXMark, and demonstrate attack success rates of up to 92%. We further discuss approaches that can harden streaming processing systems against our attacks without incurring high overhead.

Problem

• Extracting sensitive queries from secure stream processing systems using timing side channels
• Attacking Intel SGX-protected execution engines through operator profiling and monitoring
• Demonstrating practical query leakage with up to 92% success rate in benchmarks

Innovation

• Exploits timing side channels to extract queries
• Profiles operators offline using synthetic data models
• Hardens systems against attacks with low overhead