Collision Risk Analysis for LEO Satellites with Confidential Orbital Data
- URL: http://arxiv.org/abs/2501.09397v1
- Date: Thu, 16 Jan 2025 09:08:18 GMT
- Title: Collision Risk Analysis for LEO Satellites with Confidential Orbital Data
- Authors: Svenja Lage, Felicitas Hörmann, Felix Hanke, Michael Karl,
- Abstract summary: Collision risk analysis is essential to mitigate satellite collision risk.
This contribution proposes a solution based on fully homomorphic encryption (FHE)
In contrast to existing methods, this approach ensures that collision risk analysis can be performed on sensitive orbital data without revealing it to other parties.
- Score: 0.0
- License:
- Abstract: The growing number of satellites in low Earth orbit (LEO) has increased concerns about the risk of satellite collisions, which can ultimately result in the irretrievable loss of satellites and a growing amount of space debris. To mitigate this risk, accurate collision risk analysis is essential. However, this requires access to sensitive orbital data, which satellite operators are often unwilling to share due to privacy concerns. This contribution proposes a solution based on fully homomorphic encryption (FHE) and thus enables secure and private collision risk analysis. In contrast to existing methods, this approach ensures that collision risk analysis can be performed on sensitive orbital data without revealing it to other parties. To display the challenges and opportunities of FHE in this context, an implementation of the CKKS scheme is adapted and analyzed for its capacity to satisfy the theoretical requirements of precision and run time.
Related papers
- Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
We introduce a novel framework to detect instability in smart grids by employing only stable data.
It relies on a Generative Adversarial Network (GAN) where the generator is trained to create instability data that are used along with stable data to train the discriminator.
Our solution, tested on a dataset composed of real-world stable and unstable samples, achieve accuracy up to 97.5% in predicting grid stability and up to 98.9% in detecting adversarial attacks.
arXiv Detail & Related papers (2025-01-27T20:48:25Z) - Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.72954660774002]
Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas.
Extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks.
This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle.
arXiv Detail & Related papers (2025-01-26T10:13:51Z) - On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making [0.0]
This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs)
We propose an autonomous servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM.
arXiv Detail & Related papers (2024-09-25T17:40:37Z) - A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system.
We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
arXiv Detail & Related papers (2024-08-29T06:57:45Z) - Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science [65.77763092833348]
Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines.
While their capabilities are promising, these agents also introduce novel vulnerabilities that demand careful consideration for safety.
This paper conducts a thorough examination of vulnerabilities in LLM-based agents within scientific domains, shedding light on potential risks associated with their misuse and emphasizing the need for safety measures.
arXiv Detail & Related papers (2024-02-06T18:54:07Z) - Cooperative Probabilistic Trajectory Forecasting under Occlusion [110.4960878651584]
Occlusion-aware planning often requires communicating the information of the occluded object to the ego agent for safe navigation.
In this paper, we design an end-to-end network that cooperatively estimates the current states of occluded pedestrian in the reference frame of ego agent.
We show that the uncertainty-aware trajectory prediction of occluded pedestrian by the ego agent is almost similar to the ground truth trajectory assuming no occlusion.
arXiv Detail & Related papers (2023-12-06T05:36:52Z) - Predicting the Probability of Collision of a Satellite with Space
Debris: A Bayesian Machine Learning Approach [0.0]
Space is becoming more crowded in Low Earth Orbit due to increased space activity.
The need to consider collision avoidance as part of routine operations is evident to satellite operators.
Current procedures rely on the analysis of multiple collision warnings by human analysts.
arXiv Detail & Related papers (2023-11-17T16:41:35Z) - Taking a PEEK into YOLOv5 for Satellite Component Recognition via
Entropy-based Visual Explanations [0.0]
This paper contributes to efforts in enabling autonomous swarms of small chaser satellites for target geometry determination.
Our research explores on-orbit use of the You Only Look Once v5 (YOLOv5) object detection model trained to detect satellite components.
arXiv Detail & Related papers (2023-11-03T04:21:27Z) - Spacecraft Collision Risk Assessment with Probabilistic Programming [0.0]
Over 34,000 objects bigger than 10 cm in length are known to orbit Earth.
Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft.
We build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages.
arXiv Detail & Related papers (2020-12-18T14:26:08Z) - Spacecraft Collision Avoidance Challenge: design and results of a
machine learning competition [7.278310799048815]
This paper describes the design and results of the Spacecraft Collision Avoidance Challenge.
It discusses the challenges and lessons learned when applying machine learning methods to this problem domain.
arXiv Detail & Related papers (2020-08-07T10:05:20Z) - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning
for Non-Terrestrial Networks [51.05735925326235]
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation.
To maximize the end-to-end data rate, the satellite association and HAP location should be optimized.
We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
arXiv Detail & Related papers (2020-05-26T05:39:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.