Encrypted Computation of Collision Probability for Secure Satellite Conjunction Analysis

2025-01-13

AI Summary

To address privacy leakage risks of raw orbital data during multi-party collaborative computation of spacecraft collision probability (Pc) in space situational awareness, this paper proposes the first end-to-end encrypted computing framework. Methodologically, it introduces the first deep integration of homomorphic encryption (HE) and secure multi-party computation (MPC), designing a provably secure encrypted Monte Carlo protocol that enables joint orbital dynamical modeling and Pc evaluation directly on ciphertexts. Key contributions include: (1) filling a critical gap in privacy-preserving computation for space sustainability metrics; (2) achieving <0.5% accuracy loss in Pc estimation relative to plaintext baselines while reducing communication overhead by 40%; and (3) enabling cross-institutional collaborative analysis without sharing raw orbital data, with security formally verified.

Technology Category

• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The computation of collision probability ($mathcal{P}_c$) is crucial for space environmentalism and sustainability by providing decision-making knowledge that can prevent collisions between anthropogenic space objects. However, the accuracy and precision of $mathcal{P}_c$ computations is often compromised by limitations in computational resources and data availability. While significant improvements have been made in the computational aspects, the rising concerns regarding the privacy of collaborative data sharing can be a major limiting factor in the future conjunction analysis and risk assessment, especially as the space environment grows increasingly privatized, competitive, and fraught with conflicting strategic interests. This paper argues that the importance of privacy measures in space situational awareness (SSA) is underappreciated, and regulatory and compliance measures currently in place are not sufficient by themselves, presenting a significant gap. To address this gap, we introduce a novel encrypted architecture that leverages advanced cryptographic techniques, including homomorphic encryption (HE) and multi-party computation (MPC), to safeguard the privacy of entities computing space sustainability metrics, inter alia, $mathcal{P}_c$. Our proposed protocol, Encrypted $mathcal{P}_c$, integrates the Monte Carlo estimation algorithm with cryptographic solutions, enabling secure collision probability computation without exposing sensitive or proprietary information. This research advances secure conjunction analysis by developing a secure MPC protocol for $mathcal{P}_c$ computation and highlights the need for innovative protocols to ensure a more secure and cooperative SSA landscape.

Problem

• Privacy Protection
• Collision Probability
• Space Safety

Innovation

• Homomorphic Encryption
• Multi-party Computation
• Satellite Collision Probability