Space information networks is network systems that can receive, transmit, and process spatial information lively. It uses satellites, stratosphere airships, Unmanned Aerial Vehicles, and other platforms as the carrier. It supports high-dynamic, real-time broadband transmission of earth observations and ultra-long-distance, long-delay reliable transmission of deep space exploration. The deeper the network integration, the higher the system’s security concerns and the more likely SINs will be controlled and destroyed in terms of cybersecurity. How to integrate new IT technologies such as artificial intelligence, digital twins, and blockchain to diverse application scenarios of SINs while maintaining SIN cybersecurity will be a long-term critical technical issue. This study is a review of the security issues for space information networks. First, this paper examines space information networks’ security issues and figures out the relationship between the main security threats, services, and mechanisms. Then, this article selects secure routing and anomaly detection from many security technologies to conduct a detailed overview from two perspectives of traditional methods and artificial intelligence. Subsequently, this paper investigates anomaly detection schemes for spatial information networks and proposes a deep learning-based anomaly detection scheme. Finally, we suggest the potential research directions and opening problems of space information network security. Overall, this paper aims to give readers an overview of the newly emerging technologies in space information networks’ security issues and provide inspiration for future exploration.

Real-world industrial systems contain a large number of interconnected sensors that generate a significant amount of time series data during system operation. Performing anomaly detection on these multivariate time series data can timely find faults, prevent malicious attacks, and ensure these systems safe and reliable operation. However, the rarity of abnormal instances leads to a lack of labeled data, so the supervised machine learning methods are not applicable. Furthermore, most current techniques do not take full advantage of the spatial and temporal dependencies implied among multiple variables to detect anomalies. Hence, we propose STADN, a novel Anomaly Detection Network Using Spatial and Temporal Information. STADN models the relationship graph between variables for a graph attention network to capture the spatial dependency between variables and utilizes a long short-term memory network to mine the temporal dependency of time series to fully use the spatial and temporal information of multivariate time series. STADN predicts the future behavior of each sensor by combining the historical behavior of the sensor and its neighbors, then detects and locates anomalies according to the prediction error. Furthermore, we improve the proposed model’s ability to discriminate anomaly and regularity and expand the prediction error gap between normal and abnormal instances by reconstructing the prediction errors. We conduct experiments on two real-world datasets, and the experimental results suggested that STADN achieves state-of-the-art outperformance.

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Differential privacy is the state-of-the-art for preserving privacy and differential privacy mechanism based on Laplace distribution with mean 0 is common practice. However, privacy budget is exhausted so quick that the number of queries is not enough. In this paper, a differential privacy mechanism is proposed to optimize the number of queries for application scenario of multiple users. We isolate different users by assigning various noise distribution with non-zero mean to different users. First, in terms of privacy guarantee, the proposed mechanism is better than common practice. Second, for the utility aspect, the accuracy of proposed mechanism is analyzed from the view of data distribution's distortion and the view of noise's absolute value.INDEX TERMS Differential privacy, multiple users, non-zero mean.

Due to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information.Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention.In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state-of-the-art and to benefit future researches, we are motivated to provide this review.