Picachv: Formally Verified Data Use Policy Enforcement for Secure Data Analytics

2025-01-17

AI Summary

Data compliance analysis faces challenges including cross-system policy enforcement and unreliable verification. This paper proposes Picachv, a security monitor that automatically enforces data usage policies at the query execution plan level via relational algebra abstractions, supporting multiple frontend languages and analytical frameworks (e.g., Polars). It presents the first joint formalization—within Coq—of data compliance policies and relational algebra semantics, integrated with a trusted execution environment (TEE) to achieve end-to-end provably correct runtime compliance. Evaluated on the TPC-H benchmark, Picachv incurs low overhead while demonstrating strong portability, formal verifiability, and practical utility. Empirical results confirm its effectiveness in enabling scalable, compliant analysis of sensitive data across heterogeneous systems.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Intelligent Query Processing
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Ensuring the proper use of sensitive data in analytics under complex privacy policies is an increasingly critical challenge. Many existing approaches lack portability, verifiability, and scalability across diverse data processing frameworks. We introduce Picachv, a novel security monitor that automatically enforces data use policies. It works on relational algebra as an abstraction for program semantics, enabling policy enforcement on query plans generated by programs during execution. This approach simplifies analysis across diverse analytical operations and supports various front-end query languages. By formalizing both data use policies and relational algebra semantics in Coq, we prove that Picachv correctly enforces policies. Picachv also leverages Trusted Execution Environments (TEEs) to enhance trust in runtime, providing provable policy compliance to stakeholders that the analytical tasks comply with their data use policies. We integrated Picachv into Polars, a state-of-the-art data analytics framework, and evaluate its performance using the TPC-H benchmark. We also apply our approach to real-world use cases. Our work demonstrates the practical application of formal methods in securing data analytics, addressing key challenges.

Problem

• Data Privacy
• Complex Data Usage Rules
• Inter-system Data Movement

Innovation

• Relational Algebra
• Trusted Execution Environment (TEE)
• Formal Verification in Coq