Privacy concerns hinder the sharing and utilization of trajectory data collected from Internet of Things (IoT) devices. While local differential privacy mitigates leakage risks by perturbing data on mobile devices, existing methods primarily focus on enhancing global-level statistical usability of perturbed trajectories (such as spatial distributions and mobility transition patterns), neglecting individual-level semantic usability (such as road network consistency and traffic rule compliance). The resulting trajectories frequently contain unrealistic behaviors that violate traffic rules, severely limiting their applicability. To overcome this challenge, we propose TPIS, a trajectory protection approach with individual semantic utility under local differential privacy. First, we design a semantic-enhanced hierarchical modeling method (SEHM) that leverages traffic rule semantic information and geospatial information to model the road network as a weighted graph, supported by an R-tree index for efficient coarse-grained and fine-grained trajectory matching. Second, we propose a cascaded perturbation method combining stochastic sampling guided by the hierarchical graph model (SHG) and multi-feature fusion perturbation mechanism based on δ-location set (MFPD). SHG strategically constrains the perturbation space to preserve global-level statistical usability while ensuring rigorous privacy, and MFPD incorporates a comprehensive trajectory difference metric and δ -location set definition to generate perturbed trajectories that maintain individual-level structural integrity of real-world movement patterns. TPIS efficiently balances privacy and utility and ensures high computational efficiency. Theoretical analysis confirms its differential privacy guarantee and upper bound on perturbation error. Experiments conducted on four real-world datasets demonstrate significant improvements over existing methods, with up to 8× higher utility and 14× greater computational efficiency.

The vigorous development of the Internet of Things and cloud computing is driving resource-limited smart devices to outsource large-scale images to cloud servers for storage and retrieval. Privacy-preserving image retrieval addresses the threat of data privacy leakage without affecting the searchability of images. Existing privacy-preserving retrieval schemes use the approximate nearest neighbor search to improve the retrieval efficiency of large-scale images on the cloud server. However, these schemes suffer from reduced retrieval accuracy, difficulties in constructing encrypted index structures, and a lack of result verification support. To tackle these problems, we propose a verifiable privacy-preserving retrieval scheme for large-scale images (VPIRL) in cloud servers. We use learning with errors (LWE) theory to protect image features, achieving distance and angle preservation between encrypted features. This enables the cloud server to construct an encrypted satellite system graph for efficient and accurate retrieval of large-scale images. We also propose a privacy-preserving data verification method based on the Merkle Hash Tree and cuckoo hash to detect dishonest behaviors of the cloud server and verify the correctness and completeness of the approximate nearest neighbor retrieval results. Experimental results show that this scheme achieves retrieval and verification in milliseconds for millions of images, confirming its practicality for large-scale image retrieval.

With the advent of sixth-generation (6G) technologies and growing communication demands, Low Earth Orbit (LEO) satellite networks have become essential in modern communications. However, due to the dynamic topology and complex network state of LEO environments, existing routing methods often fail to make effective decisions, limiting transmission performance. This paper proposes a LEO satellite routing method based on incremental evolutionary graph reinforcement learning (IEGRL). To address network state perception challenges, we introduce a topological learning model using deep graph attention (DGA), which captures complex inter-satellite connectivity and resource states. Additionally, by integrating incremental evolution strategies (IES) into deep reinforcement learning (DRL), we replace sequential interactive proximal policy optimization (PPO) with global parallel ES, achieving efficient routing convergence in the highly dynamic LEO environment. Experimental results demonstrate that our IEGRL approach enhances LEO network load balancing by reducing end-to-end (E2E) network latency, decreasing packet loss, and improving throughput compared with the benchmark approaches.

Low Earth orbit (LEO) satellite networks are an important component of future 6G. However, due to the unique characteristics of the space environment—such as the complexity in modeling network states and the rapid dynamics of the network topology—existing routing methods often struggle to make appropriate routing decisions in the LEO satellite network context, which significantly limits network transmission performance. In this paper, we propose a dynamic satellite routing method based on deep graph attention and incremental evolution strategy (DGA-IES). Firstly, to address the challenge of accurately perceiving satellite network information, we introduce a topological perception learning model based on deep graph attention. By combining an enhanced message passing process with a self-attention mechanism, this model effectively captures complex features of the LEO network state, including inter-satellite connectivity relationships, as well as the resource states of satellites and links. Secondly, to tackle the problem of inefficient routing re-convergence in rapidly changing topologies, this paper integrates evolution strategies (ES) into deep reinforcement learning (DRL) approaches. We use the global parallel processing capabilities of ES to replace the sequential interactive proximal policy optimization (PPO) strategy in existing DRL. Moreover, we design an incremental evolutionary process based on satellite motion patterns, facilitating efficient routing convergence in highly dynamic satellite environments. Experimental results demonstrate that our DGA-IES approach enhances LEO network load balancing by reducing end-to-end (E2E) network latency by 10.3% 58.1%, decreasing packet loss by 3.8% 20.0%, and improving throughput by 11.1% 57.0% compared with the benchmark approaches.

Satellite edge computing (SEC) extends the capabilities of edge computing technology to satellite networks, facilitating rapid local processing of global task requirements. Deep reinforcement learning (DRL) has emerged as a promising approach for SEC scenarios due to its inherent dynamic adaptability, complex state modeling capability, and long-term optimization potential. However, existing DRL-based computing offloading techniques continue to encounter challenges including low sample efficiency, poor decision quality, and insufficient long-term stability, which constrain their performance in real satellite network environments. To address these challenges, this study proposes a diffusion and DRL-based approach for computation offloading in SEC networks called the generative artificial intelligence-DRL (GenAI-DRL). First, by implementing the cooperative computing model of the multi-SEC, this study comprehensively considers the heterogeneous computing and communication capabilities of satellite nodes, diversity of task types, and dynamic distribution of resources in an offloading strategy, thereby ensuring long-term system sustainability under dynamic resource constraints and provides a solid foundation for computation offloading in satellite networks with time-varying resource. Second, we integrate generative diffusion modeling (GDM) into the DRL framework to enhance policy generation by producing contextually relevant and high-quality action samples. This not only reduces the dependence on large-scale training data but also improves decision precision and generalization in complex, high-dimensional environments. Finally, a Lyapunov optimization framework is introduced to transform the offloading problem into an online per-slot optimization process, thereby ensuring the long-term stability of the SEC system under dynamic and unpredictable task arrivals and environmental conditions. The experimental results demonstrate that the method proposed offers significant advantages over the existing approaches in reducing task latency and enhancing system stability.

Cloud-based indoor positioning services have advantages over non-cloud methods but also confront serious privacy concerns. Existing privacy-preserving schemes are designed for conventional two-entity localization models thus not applicable to the cloud-based indoor positioning services involving three entities. In addition, these methods incur high computational and communication overhead. To tackle these issues, we proposed a privacy-preserving indoor positioning scheme for WiFi localization based on Inner Product Encryption in a cloud environment. A bloom filter constructed with Locality Sensitive Hashing was designed to map WiFi fingerprints from Euclidean to inner product space with the distance relationships maintained for converting the location estimation to inner product calculations. Inner Product Encryption protects the user’s fingerprint and database information held by the positioning service provider. Fingerprint similarity as determined by the inner product is decrypted on the cloud to retrieve the closest encrypted location coordinates for users. In addition, a retrieval structure based on Hierarchical Navigable Small World graph was designed to improve efficiency. Theoretical analysis and experimental results demonstrate that the scheme has low computational and communication overhead while ensuring security and not significantly degrading the localization accuracy. Moreover, the overhead does not increase significantly with database size thus this approach is highly scalable.

Cellular traffic prediction will play a key role in the deployment of future smart cities. Although the current traffic prediction methods based on deep learning show better performance than traditional prediction methods, they still have the following problems: (1) In spatial domain, the correlations between cellular traffic features cannot be captured accurately in non-local (including “geographic adjacency” and long-distance) spatial areas. (2) In temporal domain, the correlation of different time-grained features is failed to consider. To address these problems, a deep learning method considering dynamic non-local spatial correlation, self-attention, and correlation of spatio-temporal feature fusion is proposed. In spatial domain, our method can accurately capture the spatial correlation and highlight the contribution of more relevant traffic in the non-local area by designing a NLG-NLAM model. In temporal domain, the correlations of time-periodic features with different granularities are considered to clarify the key roles of different periodic features and eliminate the influence of irrelevant cellular traffic features on the prediction by designing a calibration layer. Experimental results indicate that the proposed method shows better performance than other mainstream prediction methods on three real-world cellular traffic datasets.

Users outsource images to edge servers physically closer to their location for real time applications because of the low latency and low transmission overhead. Outsourcing to these edge servers however, increases the risks to data privacy. Almost all existing privacy preserving image retrieval schemes utilize a single cloud server to execute retrieval tasks and provide centralized image retrieval but at high computational costs, thus are not suitable for the distributed edge environments with limited computing resources. We propose a lightweight privacy-preserving multi-source image retrieval scheme adapted specifically for the distributed edge environment. We apply high efficiency orthogonal decomposition and learning with errors (LWE) strategy to encrypt image features and construct cipher indexes and trapdoors, guaranteeing the security of the data, while reducing computational costs. The orthogonality of data ensures that the accuracy of retrieval results is not compromised by the random numbers used in the scheme. In addition, the proxy re-encryption technology is adopted to support the retrieval of multi-source images encrypted by unique data owners with different keys. A detailed performance analysis and comprehensive experiments demonstrate that our scheme guarantees data security with very high retrieval accuracy and a low computational burden, consistent with the demands of edge environments.

Determining the optimal number of endmember sources, which is also called “virtual dimensionality” (VD), is a priority for hyperspectral unmixing (HU). Although the VD estimation directly affects the HU results, it is usually solved independently of the HU process. In this article, a saliency-based autonomous endmember detection (SAED) algorithm is proposed to jointly estimate the VD in the process of endmember extraction (EE). In SAED, we first demonstrate that the abundance anomaly (AA) value is an important feature of undetected endmembers since pure pixels have larger AA values than “distractors” (i.e., mixed pixels and pure pixels of detected endmembers). Then, motivated by the fact that endmembers usually gather in certain local regions (superpixels) in the scene, due to spatial correlation, a superpixel prior is introduced in SAED to distinguish endmembers from noise. Specifically, the undetected endmembers are defined as visual stimuli in the AA subspace, the EE is formulated as a salient region detection problem, and the VD is automatically determined when there are no salient objects in the AA subspace. Since the spatial-contextual information of the endmembers is exploited during the saliency analysis, the proposed method is more robust than the spectral-only methods, which was verified using both real and synthetic hyperspectral images.

Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.

This paper focuses on the blind unmixing technique for analyzing hyperspectral images (HSIs). A joint deconvolution and blind hyperspectral unmixing (DBHU) algorithm is proposed, which is aimed at eliminating the impact of the adjacency effect (AE) on unmixing. In remote sensing imagery, the AE occurs in the presence of atmospheric scattering over a heterogeneous surface. The AE leads to blurring and additional mixing of HSIs and makes it difficult to estimate endmembers and abundances accurately. In this paper, we first model the blurred HSIs by the use of a bilinear mixing model (BMM), where a blurring kernel is used to model the mixing caused by the AE. Based on the BMM, the DBHU problem is formulated as a constrained and biconvex optimization problem. Specifically, the minimum-volume simplex (MVS) is incorporated to deal with the additional mixing caused by the AE, and 3-D total variation (TV) priors are adopted to model the spectral-spatial correlation of the data. In DBHU, the biconvex problem is efficiently solved by a nonstandard application of the alternating direction method of multipliers (ADMM) algorithm, where a block coordinate descent scheme is applied by splitting the original problem into two saddle point subproblems, and then minimizing the subproblems alternately via the ADMM until convergence. The experimental results obtained with both simulated and real data confirm the viability of the proposed algorithm, and DBHU works well, even where both blurring and noise are present in the scene.

Blind hyperspectral unmixing (HU) technique aims at identifying pure materials in a hyperspectral image (HSI), called endmembers, and quantifying the corresponding proportions, called abundances, with little prior knowledge. In this paper, the degradation mechanism during data collection - adjacent effect (AE), is considered in the process of blind HU. Since the AE leads to blurring (the loss of sharpness, contrast and apparent resolution) in scene, it blocks the quantitative analysis of HSI in sub-pixel level and makes the estimated endmembers and abundances inaccurate. To solve this problem, a bilinear mixing model is developed to simulate the AE, and a novel algorithm, termed joint deconvolution and blind HU (DBHU) is proposed. In DBHU, the bi-convex optimization problem is efficiently solved by a nonstandard application of the alternating direction method of multipliers (ADMM) algorithm, where a block coordinate descent scheme is applied by splitting the original problem into two saddle-point subproblems and then minimizing the subproblems alternatively via ADMM until convergence. The experimental results on both simulated and real HSI illustrate the viability of the proposed algorithm.

This paper focuses on the endmember extraction (EE) technique for analyzing hyperspectral images. We first prove that the reconstruction errors (REs) and abundance anomalies (AAs) (abundances that fail to satisfy the abundance constraints) are effective in extracting undetected endmembers. Then, according to the spatial continuity of the endmember objects and differing from noise or outliers with a sparse distribution, the endmembers are assumed to be located at some salient areas in the RE and AA maps. A novel EE algorithm termed saliency-based endmember detection (SED) is proposed, where the visual saliency model is introduced to explore and analyze the spatial information that is contained in the AA and RE maps. Specifically, the AA and RE maps are regarded as the visual inputs, whereas the endmembers are treated as the visual stimuli. In SED, we assume that the pure pixel assumption holds. Based on the characteristics of the human visual system, the proposed method can not only extract endmembers in homogenous areas, but it can also highlight the small targets whose abundances may be spatially varied. In addition, since the spatial information is exploited in the reconstruction, the capability of the endmembers to represent the hyperspectral scene is automatically considered in the process of EE, and the detected endmembers are both accurate and reliable. The experimental results obtained on both simulated and real hyperspectral data confirm the merits and viability of the proposed algorithm.

This paper focuses on the spectral unmixing technique for analyzing hyperspectral image (HSI). In this paper, we first prove that the reconstruction errors and the abundance anomalies (AAs, abundances that are negative or greater than one) are effective in measuring the purity of pixels. Then, due to the continuity of the objects in the space, the endmembers are assumed to be located at some noticeable areas in residual and AA maps. A saliency-based endmember detection (SED) algorithm which aims at iteratively extracting endmembers from the residual and AA maps is proposed, where the visual attention mechanism is developed to understand and analyze the spatial pattern of endmembers. In addition, when searching for new endmembers, the spectral properties are also utilized to promote the robustness of the proposed method. The experimental results on both simulated data and real hyperspectral data illustrate the merits and viability of the proposed algorithm.

The detection of frame deletion forgery is of great significance in the field of video forensics. Existing approaches, however, are not applicable to video sequences with variable motion strengths. In addition, the impact of interfering frames has not been considered in these approaches. Our research aims to develop a motion-adaptive forensic method as well as to eliminate interfering frames. Through a study of the statistical characteristics of the most common interfering frames such as relocated I-frames, we develop a new fluctuation feature based on frame motion residuals to identify frame deletion points (FDPs). The fluctuation feature is further enhanced by an intra-prediction elimination procedure so that it can be adapted to sequences with various motion levels. The enhanced feature is measured using a moving window detector to identify the location of a FDP. Finally, a postprocessing procedure is proposed to eliminate the minor interferences of sudden lighting change, focus vibration, and frame jitter. Our experimental results demonstrate that for videos with variable motion strengths and different interfering frames, the true positive rate of the algorithm can reach 90% when the false alarm rate is 0.3%. Our proposed method could provide a foundation for many practical applications of video forensics.

In recent years, blind source separation (BSS) has received much attention in the hyperspectral unmixing field due to the fact that it allows the simultaneous estimation of both endmembers and fractional abundances. Although great performances can be obtained by the BSS-based unmixing methods, the decomposition results are still unstable and sensitive to noise. Motivated by the first law of geography, some recent studies have revealed that spatial information can lead to an improvement in the decomposition stability. In this paper, the group-structured prior information of hyperspectral images is incorporated into the nonnegative matrix factorization optimization, where the data are organized into spatial groups. Pixels within a local spatial group are expected to share the same sparse structure in the low-rank matrix (abundance). To fully exploit the group structure, image segmentation is introduced to generate the spatial groups. Instead of a predefined group with a regular shape (e.g., a cross or a square window), the spatial groups are adaptively represented by superpixels. Moreover, the spatial group structure and sparsity of the abundance are integrated as a modified mixed-norm regularization to exploit the shared sparse pattern, and to avoid the loss of spatial details within a spatial group. The experimental results obtained with both simulated and real hyperspectral data confirm the high efficiency and precision of the proposed algorithm.

Cloud-based file systems are widely accepted and adopted for personal and business purposes in recent years. Statistics shows that ∼25% of file operations from a typical user are random writes. Inherited from traditional disk-based file systems, most distributed file systems are also based on objects or chunks of fixed sizes, which work well for sequential writes but poorly for random writes. This paper investigates the design paradigm of variable-sized objects for a distributed file system, where a new file write interface is proposed to provide rich write semantics. A novel distributed file system named VarFS, is presented to incorporate variable object indexing, support the random write interface and remain POSIX compatible. VarFS reduces the amount of unnecessary data being read and the number of objects modified in face of updates and consequently alleviates the total amount of data transferred. VarFS is implemented based on Ceph and the performance measurements show that it can achieve 1–2 orders of magnitude less latency than Ceph on random writes. At the same time, the overhead for initial writes and re-writes is acceptable.

SUMMARY Cloud computing as a promising technology and paradigm can provide various data services, such as data sharing and distribution, which allows users to derive benefits without the need for deep knowledge about them. However, the popular cloud data services also bring forth many new data security and privacy challenges. Cloud service provider untrusted, outsourced data security, hence collusion attacks from cloud service providers and data users become extremely challenging issues. To resolve these issues, we design the basic parts of secure re‐encryption scheme for data services in a cloud computing environment, and further propose an efficient and secure re‐encryption algorithm based on the EIGamal algorithm, to satisfy basic security requirements. The proposed scheme not only makes full use of the powerful processing ability of cloud computing but also can effectively ensure cloud data security. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security model. Copyright © 2015 John Wiley & Sons, Ltd.

Detection of frame deletion is of great significance in the field of video forensics. Several approaches have been presented through analyzing the side effect caused by frame deletion. However, most of the current approaches can detect the existence of frame deletion but not the exact location of it. In this paper, we present a method which can directly locate the frame deletion point. Through the analysis of the distinguishing fluctuation feature of motion residual caused by frame deletion compared to interference frames and ordinary video content jitter in tampered video sequence, an algorithm based on the total motion residual of video frame is proposed to detect the frame deletion point. Moreover, an initiative processing procedure for frame motion residual and an adaptive threshold detector are introduced so that the robustness of the detection can be markedly improved. Experimental results show that the proposed algorithm is effective in generalized scenarios such as different encoding settings, rapid or slow motion sequences and multiple group of picture deletion. It also has a high performance that the true positive rate reaches 90% and the false alarm rate is less than 0.8%.

For the special characters, remote sensing image has higher requirements in content security: it desires not only the encryption during storage and transmission for preventing information leakage, but also the watermarking after illegal usage for copyright protection or even source tracing. Therefore, this paper proposed to integrate encryption and watermarking based on the orthogonal decomposition for the comprehensive security protection of remote sensing image. By the proposed method, encryption and watermarking can achieve the operation independence and the content mergence; moreover, there is not special requirement in selecting special encryption and watermarking algorithms. It makes up the shortage of recent integration method based on spatial scrambling in application and possesses higher security. According to the experimental results, the proposed method satisfies the common constraints of encryption and watermarking, furthermore, has little impact on remote sensing image data characters and later applications.