Search Results (52)

With the rapid development of information technology and the deepening of digitalization, operating systems are increasingly applied in critical information infrastructure, making data security issues particularly important. Traditional cloud storage auditing models based on third-party auditing authorities (TPA) face trust risks and potential data leakage during data integrity verification, which makes them inadequate to meet the dual requirements of high security and local controllability in the current information technology environment. To address this, this paper proposes a system-wide data security auditing method for the Chinese Linux operating system, constructing a lightweight and localized framework for sensitive information protection and auditing. By dynamically intercepting system calls and performing real-time content analysis, the method achieves accurate identification and visual masking of sensitive information, while generating corresponding audit logs. To overcome the efficiency bottleneck of traditional pattern matching in high-concurrency environments, this paper introduces a Chinese Aho-Corasick (AC) automaton-based character matching algorithm using a hash table to enhance the rapid retrieval capability of sensitive information. Experimental results demonstrate that the proposed method not only ensures controllable and auditable sensitive information but also maintains low system overhead and good adaptability, thereby providing a feasible technical path and implementation scheme for data security. Full article

Ensuring the integrity of medical images is essential to securing clinical workflows, telemedicine platforms, and healthcare IoT environments. Existing watermarking and reversible data-hiding approaches often modify pixel intensities, reducing diagnostic fidelity, introducing embedding constraints, or causing instability under compression and format conversion. This work proposes a distortion-free, non-embedding authentication framework that leverages the inherent stability of the most significant bit (MSB) patterns in the Non-Region of Interest (NROI) to construct a secure and tamper-sensitive reference for the diagnostic Region of Interest (ROI). The ROI is partitioned into fixed blocks, each producing a 256-bit SHA-256 signature. Instead of embedding this signature, each hash bit is mapped to an NROI pixel whose MSB matches the corresponding bit value, and only the encrypted coordinates of these pixels are stored externally in a secure database. During verification, hashes are recomputed and compared bit-by-bit with the MSB sequence extracted from the referenced NROI coordinates, enabling precise block-level tamper localization without modifying the image. Extensive experiments conducted on MRI (OASIS), X-ray (ChestX-ray14), and CT (CT-ORG) datasets demonstrate the following: (i) perfect zero-distortion fidelity; (ii) stable and deterministic MSB-class mapping with abundant coordinate diversity; (iii) 100% detection of intentional ROI tampering with no false positives across the six clinically relevant manipulation types; and (iv) robustness to common benign Non-ROI operations. The results show that the proposed scheme offers a practical, secure, and computationally lightweight solution for medical image integrity verification in PACS systems, cloud-based archives, and healthcare IoT applications, while avoiding the limitations of embedding-based methods. Full article

In edge computing scenarios, the data generated by distributed devices is characterized by its dispersion, heterogeneity, and privacy sensitivity, posing significant challenges to federated clustering, including high communication overhead, difficulty in adapting to non-IID data, and significant privacy leakage risks. To address these issues, this paper proposes a privacy-enhanced federated k-means clustering algorithm based on locality-sensitive hashing, aiming to mine latent knowledge from multi-source distributed data while ensuring data privacy protection. The core innovation of this algorithm lies in leveraging the distance sensitivity of clustering pairs, which effectively mitigates the non-IID problem while preserving data privacy and achieves global clustering in just a single communication round, significantly enhancing its practicality in communication-constrained environments. Specifically, the algorithm first evaluates local data dispersion at the client side, dynamically generates cluster cardinality based on dispersion, and obtains initial clustering centers through the k-means algorithm. Subsequently, it employs locality-sensitive hashing to encrypt the center points, uploading only the encrypted clustering information and weight data to the server, thereby achieving privacy protection without relying on a trusted server. On the server side, a secondary weighted k-means clustering is performed in the encrypted space to generate hashed global centers. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that this method maintains robust clustering performance under non-IID data distributions. Most crucially, through a strict single-round client-to-server communication protocol, this approach significantly reduces communication overhead, providing a distributed data mining solution that is efficient, adaptable, and privacy-preserving for resource-constrained edge computing environments. Full article

The Learning with Errors (LWE) problem, particularly its efficient ternary variant where secrets and errors are small, is a fundamental building block for numerous post-quantum cryptographic schemes. Combinatorial attacks provide a potent approach to cryptanalyzing ternary LWE. While classical attacks have achieved complexities close to their asymptotic ${\mathcal{S}}^{0.25}$ bound for a search space of size $\mathcal{S}$, their quantum counterparts have faced a significant gap: the attack by van Hoof et al. (vHKM) only reached a concrete complexity of ${\mathcal{S}}^{0.251}$, far from its asymptotic promise of ${\mathcal{S}}^{0.193}$. This work introduces an efficient quantum combinatorial attack that substantially narrows this gap. We present a quantum walk adaptation of the locality-sensitive hashing algorithm by Kirshanova and May, which fundamentally removes the need for guessing error coordinates—the primary source of inefficiency in the vHKM approach. This crucial improvement allows our attack to achieve a concrete complexity of approximately ${\mathcal{S}}^{0.225}$, markedly improving over prior quantum combinatorial methods. For concrete parameters of major schemes including NTRU, BLISS, and GLP, our method demonstrates substantial runtime improvements over the vHKM attack, achieving speedup factors ranging from $2^{16}$ to $2^{60}$ across different parameter sets and establishing the new state-of-the-art for quantum combinatorial attacks. As a second contribution, we address the challenge of polynomial quantum memory constraints. We develop a hybrid approach combining the Kirshanova–May framework with a quantum claw-finding technique, requiring only $\mathcal{O}\left(n\right)$ qubits while utilizing exponential classical memory. This work provides the first comprehensive concrete security analysis of real-world LWE-based schemes under such practical quantum resource constraints, offering crucial insights for post-quantum security assessments. Our results reveal a nuanced landscape where our combinatorial attacks are superior for small-weight parameters, while lattice-based attacks maintain an advantage for others. Full article

As digital images proliferate across open networks, securing them against unauthorized access has become imperative. However, many recent image encryption algorithms are limited by weak chaotic dynamics and inadequate cryptographic design. To overcome these, we propose a new 2D coupling-enhanced cubic hyperchaotic map with exponential parameters (2D-CCHM-EP). By incorporating exponential terms and strengthening interdependence among state variables, the 2D-CCHM-EP exhibits strict local expansiveness, effectively suppresses periodic windows, and achieves robust hyperchaotic behavior, validated both theoretically and numerically. It outperforms several recent chaotic maps in key metrics, yielding significantly higher Lyapunov exponents and Kolmogorov–Sinai entropy, and passes all NIST SP 800-22 randomness tests. Leveraging the 2D-CCHM-EP, we further develop a hierarchical significance-aware multi-image encryption algorithm (MIEA-CPHS). The core of MIEA-CPHS is a hierarchical significance-aware encryption strategy that decomposes input images into high-, medium-, and low-significance layers, which undergo three, two, and one round of vector-level adaptive encryption operations. An SHA-384-based hash of the fused data dynamically generates a 48-bit adaptive control parameter, enhancing plaintext sensitivity and enabling integrity verification. Comprehensive security analyses confirm the exceptional performance of MIEA-CPHS: near-zero inter-pixel correlation (<0.0016), near-ideal Shannon entropy (>7.999), and superior plaintext sensitivity (NPCR $\approx 99.61\%$, UACI $\approx 33.46\%$). Remarkably, the hierarchical design and vectorized operations achieve an average encryption throughput of 87.6152 Mbps, striking an outstanding balance between high security and computational efficiency. This makes MIEA-CPHS highly suitable for modern high-throughput applications such as secure cloud storage and real-time media transmission. Full article

Botnet attacks on Internet of Things (IoT) devices are escalating at the 5G/6G multi-access edge, yet most federated learning frameworks for IoT malware detection (FL-IMD) still hinge on a central aggregator, enlarging the attack surface, weakening privacy, and creating a single point of failure. We propose a two-tier, fully decentralized FL architecture aligned with MEC’s Proximal Edge Server (PES)/Supplementary Edge Server (SES) hierarchy. PES nodes train locally and encrypt updates with the Cheon–Kim–Kim–Song (CKKS) scheme; SES nodes verify ECDSA-signed provenance, homomorphically aggregate ciphertexts, and finalize each round via an Algorand-style committee that writes a compact, tamper-evident record (update digests/URIs and a global-model hash) to an append-only ledger. Using the N-BaIoT benchmark with an unsupervised autoencoder, we evaluate known-device and leave-one-device-out regimes against a classical centralized baseline and a cryptographically hardened but server-centric variant. With the heavier CKKS profile, attack sensitivity is preserved (TPR $\ge 0.99$), and specificity (TNR) declines by only 0.20 percentage points relative to plaintext in both regimes; a lighter profile maintains TPR while trading 3.5–4.8 percentage points of TNR for about 71% smaller payloads. Decentralization adds only a negligible per-round overhead for committee finality, while homomorphic aggregation dominates latency. Overall, our FL-IMD design removes the trusted aggregator and provides verifiable, ledger-backed provenance suitable for trustless MEC deployments. Full article

Large language models (LLMs) have driven transformative progress in artificial intelligence, yet critical challenges persist in data management and privacy protection during model deployment and training. The approximate nearest neighbor (ANN) search, a core operation in LLMs, faces inherent trade-offs between efficiency and security when implemented through conventional locality-sensitive hashing (LSH)-based secure ANN (SANN) methods, which often compromise either query accuracy due to false positives. To address these limitations, this paper proposes a novel secure ANN scheme based on nearest neighbor graph (NNG-SANN), which is designed to ensure the security of approximate k-nearest neighbor queries for vector data commonly used in LLMs. Specifically, a secure indexing structure and subset partitioning method are proposed based on LSH and NNG. The approach utilizes neighborhood information stored in the NNG to supplement subset data, significantly reducing the impact of false positive points generated by LSH on query results, thereby effectively improving query accuracy. To ensure data privacy, we incorporate a symmetric encryption algorithm that encrypts the data subsets obtained through greedy partitioning before storing them on the server, providing robust security guarantees. Furthermore, we construct a secure index table that enables complete candidate set retrieval through a single query, ensuring our solution completes the search process in one interaction while minimizing communication costs. Comprehensive experiments conducted on two datasets of different scales demonstrate that our proposed method outperforms existing state-of-the-art algorithms in terms of both query accuracy and security, effectively meeting the precision and security requirements for nearest neighbor queries in LLMs. Full article

With the continuous expansion of users, businesses, and services in electricity retail trading systems, the demand for personalized recommendations has grown significantly. To address the issue of reduced recommendation accuracy caused by insufficient data in standalone recommendation systems, the academic community has conducted in-depth research on distributed recommendation systems. However, this collaborative recommendation environment faces two critical challenges: first, how to effectively protect the privacy of data providers and power users during the recommendation process; second, how to handle the potential presence of malicious data providers who may supply false recommendation data, thereby compromising the system’s reliability. To tackle these challenges, a blockchain-based lightweight reputation-aware electricity retail trading service recommendation (BLR-ERTS) system is proposed, tailored for electricity retail trading scenarios. The system innovatively introduces a recommendation method based on Locality-Sensitive Hashing (LSH) to enhance user privacy protection. Additionally, a reputation management mechanism is designed to identify and mitigate malicious data providers, ensuring the quality and trustworthiness of the recommendations. Through theoretical analysis, the security characteristics and privacy-preserving capabilities of the proposed system are explored. Experimental results show that BLR-ERTS achieves an MAE of 0.52, MSE of 0.275, and RMSE of 0.52 in recommendation accuracy. Compared with existing baseline methods, BLR-ERTS improves MAE, MSE, and RMSE by approximately 13%, 14%, and 13%, respectively. Moreover, the system exhibits 94% efficiency, outperforming comparable approaches by 4–24%, and maintains robustness with only a 30% attack success rate under adversarial conditions. The findings demonstrate that BLR-ERTS not only meets privacy protection requirements but also significantly improves recommendation accuracy and system robustness, making it a highly effective solution in a multi-party collaborative environment. Full article

As the Internet of Vehicles (IoV) rapidly gains popularity, the Controller Area Network (CAN) faces increasingly severe security threats. Most of the existing research on protecting the CAN bus has been based on artificial intelligence models, which require complex feature extraction and training processes and are too resource-intensive for deployment in resource-constrained CAN environments. To address these challenges, we propose a lightweight intrusion detection system based on locality-sensitive hashing that achieves efficient security protection without relying on complex machine learning and deep learning frameworks. We employ the Nilsimsa algorithm to compute hash digests of the data, using the similarity scores of these digests as anomaly scores to identify abnormal traffic. Evaluations show that our method achieves an accuracy of 98%, and tests of the system’s overhead confirm its suitability for deployment in resource-limited CAN scenarios. Full article

Similarity-based decision support systems have become essential tools for providing tailored and adaptive guidance across various domains. In agriculture, where managing extensive land areas poses significant challenges, the primary objective is often to maximize harvest yields while reducing costs, preserving crop health, and minimizing the use of chemical adjuvants. The application of similarity-based analysis enables the development of personalized farming recommendations, refined through shared data and insights, which contribute to improved plant growth and enhanced annual harvest outcomes. This study employs two algorithms, K-Nearest Neighbour (KNN) and Approximate Nearest Neighbour (ANN) using Locality Sensitive Hashing (LSH) to evaluate their effectiveness in agricultural decision-making. The results demonstrate that, under comparable farming conditions, KNN yields more accurate recommendations due to its reliance on exact matches, whereas ANN provides a more scalable solution well-suited for large datasets. Both approaches support improved agricultural decisions and promote more sustainable farming strategies. While KNN is more effective for smaller datasets, ANN proves advantageous in real-time applications that demand fast response times. The implementation of these algorithms represents a significant advancement toward data-driven and efficient agricultural practices. Full article

When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the LSH algorithm with the robust fitting mechanism of RANSAC. With proper hash functions designed, similar data points are mapped to the same hash bucket, thereby enabling the efficient identification and removal of outliers. RANSAC is then used to fit the model parameters of the processed dataset. The optimal parameters for the linear model are obtained after multiple iterative processes. This algorithm significantly reduces the influence of outliers on the dataset, resulting in improved fitting accuracy and enhanced robustness. Experimental results demonstrate that the proposed improved RANSAC linear fitting algorithm outperforms the Weighted Least Squares, traditional RANSAC, and Maximum Likelihood Estimation methods, achieving a reduction in the sum of squared residuals by 29%, 16%, and 8%, respectively. Full article

The Multiple Random Empirical Kernel Learning Machine (MREKLM) typically generates multiple empirical feature spaces by selecting a limited group of samples, which helps reduce training duration. However, MREKLM does not incorporate data distribution information during the projection process, leading to inconsistent performance and issues with reproducibility. To address this limitation, we introduce a within-class scatter matrix that leverages the distribution of samples, resulting in the development of the Fast Multiple Empirical Kernel Learning Incorporating Data Distribution Information (FMEKL-DDI). This approach enables the algorithm to incorporate sample distribution data during projection, improving the decision boundary and enhancing classification accuracy. To further minimize sample selection time, we employ a border point selection technique utilizing locality-sensitive hashing (BPLSH), which helps in efficiently picking samples for feature space development. The experimental results from various datasets demonstrate that FMEKL-DDI significantly improves classification accuracy while reducing training duration, thereby providing a more efficient approach with strong generalization performance. Full article

Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains. Full article

With the advent of cloud computing, outsourced computing has emerged as an increasingly popular strategy to reduce the burden of local computation. Optimal location query (OLQ) is a computationally intensive task in the domain of big data outsourcing, which is designed to determine the optimal placement of a new facility from a set of candidate locations. However, location data are sensitive and cannot be shared with other enterprises, so privacy-preserving optimal location query becomes particularly important. Although some privacy-preserving works have been proposed, they still suffer from other challenges, such as irrevocable query permissions and high communication overhead. To overcome these challenges, we propose a revocable and privacy-preserving optimal location query scheme based on TEE (Trusted Execution Environment). We employ a basic hash structure within the TEE to compute the intersection data of both parties. We use the concept of reverse nearest neighbor (RNN) to assess the impact of candidates, and then select the optimal facility location. In addition, to implement the revocation of query permissions, we introduce a key refresh strategy that adopts identity and timestamp. We evaluate the performance of the proposed scheme using real datasets, and the experimental results indicate strong practicality. Full article

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in buildings leads to insufficient data sampling for model training, which is currently an important factor affecting the performance of short-term electrical load prediction. To address this issue, by using peak data clustering and knowledge transfer from similar buildings, a short-term electrical load forecasting method is proposed. First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. Second, for peak/valley data clusters, an instance-based transfer learning (IBTL) strategy is used to transfer similar data from multi-source domains to enhance the target prediction’s accuracy. During the process, a two-stage similar data selection strategy is applied based on Wasserstein distance and locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, is designed to construct the predictive model. The performance of proposed method is validated on a public dataset. Results show that the data clustering and transfer learning method reduces the error by 49.22% (MAE) compared to the Elman model. Compared to the same transfer learning model without data clustering, the proposed approach also achieves higher prediction accuracy (1.96% vs. 2.63%, MAPE). The proposed method is also applied to forecast hourly/daily power demands of two real campus buildings in the USA and China, respectively. The effects of data clustering and knowledge transfer are both analyzed and compared in detail. Full article