Enabling decision support over confidential data

2025-09-02

AI Summary

In multi-party distributed settings involving sensitive data, decision support systems (DSS) struggle to simultaneously guarantee confidentiality, verifiability, transparency, integrity, and consistency. To address this, we propose SPARTA—a novel architecture that pioneers the deep integration of Trusted Execution Environments (TEEs) with Verifiable Software Objects (VSOs), enabling policy-driven, confidential decision outputs customizable by end users. SPARTA leverages TEEs to protect both computation logic and data privacy, while combining cryptographic encryption, notarized data protection, and policy-based access control to ensure end-to-end verifiability, result consistency, and fine-grained output control. Extensive experiments on public benchmarks and synthetic datasets demonstrate SPARTA’s efficiency, scalability, and practicality, significantly enhancing the security and trustworthiness of automated decision-making over heterogeneous, confidential data sources.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Distributed Problem Solving

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Enabling automated decision-making processes by leveraging data-driven analysis is a core goal of Decision Support Systems (DSSs). In multi-party scenarios where decisions rely on distributed and sensitive data, though, ensuring confidentiality, verifiability, transparency, integrity, and consistency at once remains an open challenge for DSSs. To tackle this multi-faceted problem, we propose the Secure Platform for Automated decision Rules via Trusted Applications (SPARTA) approach. By leveraging Trusted Execution Environments (TEEs) at its core, SPARTA ensures that the decision logic and the data remain protected. To guarantee transparency and consistency of the decision process, SPARTA encodes decision rules into verifiable software objects deployed within TEEs. To maintain the confidentiality of the outcomes while keeping the information integrity, SPARTA employs cryptography techniques on notarized data based on user-definable access policies. Based on experiments conducted on public benchmarks and synthetic data, we find our approach to be practically applicable and scalable.

Problem

• Ensuring confidentiality in multi-party decision support systems
• Maintaining verifiability and transparency over distributed sensitive data
• Protecting decision logic integrity with trusted execution environments

Innovation

• Uses Trusted Execution Environments for data protection
• Encodes decision rules into verifiable software objects
• Employs cryptography with user-definable access policies