Search Results (65)

Cross-border blockchain investigations frequently face data isolation challenges where multiple jurisdictions may conduct parallel inquiries into the same suspicious entities, leading to operational conflicts and redundant efforts. This paper presents a purpose-built t-out-of-n watchlist-anchored private set intersection (PSI) protocol, adapting established threshold secret-sharing techniques for secure jurisdictional discovery, enabling agencies to identify overlapping investigative targets without prematurely disclosing sensitive case details. The methodology is built upon Shamir’s Secret Sharing (SSS) and polynomial interpolation over the $2^{127}-1$ Mersenne prime field. A deterministic dual-hash field mapping ensures statistical uniformity over the prime field. Experimental validation using the Elliptic++ dataset confirmed the system’s high efficiency. The protocol maintains linear communication complexity of $O(n·|{\mathcal{S}}_0\left|\right)$, where complexity scales with the watchlist size rather than the full participant dataset and remains stable under varying consensus requirements, where increasing the threshold t results in a marginal increase in total latency. Under the semi-honest adversarial model, the false-positive rate is cryptographically negligible at $2^{-127}$. The protocol achieves a hybrid security model wherein share privacy is information-theoretic under SSS, while field mapping and share authentication rely on standard computational assumptions. By integrating native source traceability, this framework provides a practical technological foundation for initiating formal Mutual Legal Assistance Treaty (MLAT) requests based on confidential matches identified across independent investigative workflows. Full article

Under the paradigm of the Internet of Things (IoT), the processing of large-scale data not only imposes higher demands on data-sharing efficiency but also increases the risk of user privacy leakage. To address these challenges, this paper proposes a blockchain-assisted traceable and revocable broadcast encryption scheme for preventing malicious encryptors (BATR). To resist trapdoor attacks by malicious encryptors, the scheme utilizes the uniform distribution property of hash function outputs to generate the random numbers required for the encryption algorithm. To block malicious users from leaking private keys, which attackers could exploit to construct piracy decoders with decryption capabilities, the scheme enhances the traditional broadcast encryption system by incorporating public tracing and revocation mechanisms. The scheme employs personalized transmission technology, allowing data owners to share public data with a set of authorized users while also sharing personalized data with specific authorized users. Additionally, users communicate using pseudonyms to ensure that their real identities are not accessible to third parties, thereby meeting privacy protection requirements. With the assistance of blockchain, trusted authorities and users can invoke smart contract interfaces to trigger blockchain peer nodes to execute smart contracts, thereby acquiring or updating identity authentication information stored on the blockchain to achieve secure authentication. This paper provides an analysis of the correctness and security of BATR, demonstrating that BATR satisfies chosen-ciphertext security under the Random Oracle Model. We also present performance evaluations and describe the experimental setup used to obtain operation-time baselines. Finally, this paper conducts a performance analysis of the BATR scheme, which exhibits high computational efficiency and compact communication bandwidth, resulting in significant performance improvements. Full article

AI-driven personalization (AIP) has become a core mechanism of digital commerce platforms, yet its psychological consequences remain theoretically fragmented. Drawing on the Stimulus–Organism–Response (SOR) framework and Psychological Reactance Theory (PRT), this study proposes a Threat-Substitution Mechanism (TSM) to explain how AIP shapes continuance intention in high-involvement online travel decisions. Using survey data from 488 Generation Y and Z users of Chinese online travel agencies and analyzing the model via PLS-SEM, results show that AIP significantly increases usage intention (UI) and reduces psychological reactance. Psychological reactance partially mediates the relationship between AIP and UI, indicating the presence of underlying psychological friction alongside dominant utilitarian benefits. Furthermore, privacy concerns amplify the negative relationship between AIP and reactance, suggesting that privacy-sensitive users exhibit heightened appraisal sensitivity rather than uniform resistance to personalization. By reconceptualizing the personalization paradox as a context-contingent threat appraisal process, this study advances electronic commerce research beyond parallel dual-effect models and clarifies the boundary conditions under which AIP enhances or constrains user continuance. Practical implications highlight the importance of algorithmic precision and autonomy-supportive design in AI-enabled commerce platforms. Full article

The collection of graph data in decentralized environments raises critical privacy concerns. Local differential privacy (LDP) provides a strong privacy model but applying uniform noise to all graph elements often destroys utility. To address this, we propose the Criticality-Aware Adaptive Local Differential Privacy (CA-LDP) framework. CA-LDP quantifies the structural importance of nodes and edges, then dynamically allocates privacy budgets: stronger protection for critical structures, and weaker protection for non-critical ones. Users report a low-dimensional criticality-weighted vector and, for a subset of critical edges, a precise randomized response. The server reconstructs a synthetic graph using an enhanced graph structure learning model that integrates these criticality signals. A theoretical analysis proves CA-LDP satisfies $\epsilon$-LDP. Experiments on real-world datasets show that CA-LDP outperforms state-of-the-art baselines in preserving critical edges and node classification accuracy, while effectively reducing the success rate of link inference attacks on critical structures. Full article

We propose stateful order-preserving encryption (SOPE), a novel framework designed to realize human-centric data security and privacy, the fundamental values of the Fifth Industrial Revolution. Conventional order-preserving encryption supports efficient queries in cloud databases but fundamentally leaks plaintext distributions, leaving data vulnerable to inference attacks. To mitigate this vulnerability while maintaining query efficiency, SOPE introduces a partition-based dynamic density adjustment mechanism under an honest-but-curious threat model. This mechanism offsets density imbalances between partitions in real time by inserting decoy ciphertexts, thereby limiting the leakage scope to the order of data while obscuring frequency information. Our analysis and empirical evaluations demonstrate that SOPE’s ciphertexts consistently approach a uniform distribution by adaptively compensating for the underlying plaintext distribution through decoy insertion. While the continuous insertion of decoy ciphertexts inevitably incurs additional storage overhead (controlled by a tunable parameter $\lambda$), our evaluations demonstrate practical performance. By striking an optimal balance between efficiency and human privacy rights, SOPE provides a trustworthy infrastructure for secure data utilization. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article

As a privacy-preserving distributed machine learning paradigm, federated learning (FL) faces serious communication bottlenecks in practical deployment. In this paper, we propose FedLayerPrune, a communication-efficient federated learning method that integrates three synergistic components: (i) a layer-adaptive pruning strategy that dynamically allocates pruning rates based on layer sensitivity and network depth; (ii) a heterogeneity-aware aggregation mechanism that combines sample-size weighted averaging with mask consensus voting to enhance robustness under non-IID data distributions; and (iii) a dynamic pruning rate scheduler that progressively increases compression intensity across training rounds. Unlike existing approaches that apply uniform pruning or consider these techniques in isolation, FedLayerPrune achieves a principled coordination among layer-wise importance evaluation, temporal pruning scheduling, and heterogeneous model aggregation. Extensive experiments on CIFAR-10, MNIST, and Fashion-MNIST demonstrate that FedLayerPrune reduces communication costs by up to 68.3% compared with standard FedAvg, while maintaining model accuracy within a 2% margin. Moreover, our method exhibits stronger robustness and faster convergence under severe non-IID data distributions. These results suggest that FedLayerPrune provides a practical and effective solution for deploying federated learning in resource-constrained edge computing environments. Full article

Existing Bayesian network-based differential privacy algorithms predominantly employ uniform privacy budget allocation. However, since attribute nodes carry heterogeneous information loads, the traditional privacy budget allocation strategy may result in insufficient noise being added to important attributes, while excessive noise is added to less important attributes. To optimize privacy budget utilization, we propose SA-PrivBayes, a scoring-function-driven allocation method. To enhance Bayesian network precision, we introduce a threshold mechanism during network construction that pre-filters low-scoring attribute pairs before applying the exponential mechanism for selection. Subsequently, during parameter learning, privacy budgets are dynamically allocated to low-dimensional attribute sets based on node-specific scoring functions. Under identical privacy budgets, our algorithm demonstrates stronger data protection capabilities compared to the PrivBayes algorithm. Experimental results indicate that, compared to traditional differential privacy methods based on Bayesian networks under identical privacy budgets, our algorithm better meets the privacy protection requirements of high-dimensional data while maintaining higher data utility. Full article

Local Differential Privacy (LDP) and its personalized variants (PLDP) have been widely used for privacy-preserving data analytics. However, existing schemes often enforce a uniform indistinguishability level among users, failing to accommodate the nuanced privacy needs of diverse individuals. To address this, we propose User-Distinguished Local Differential Privacy (UDPLDP), a novel framework that formalizes user-level distinguishability to support more flexible, non-uniform privacy budgets. Under this framework, we tackle the fundamental task of frequency range queries, namely UDPLDP-Tree, which overcomes the challenge due to limited user-level distinguishability, insufficient robustness in estimation under complex data distributions, and the assumption of uniform privacy requirements across different attributes in existing multi-dimensional schemes. To demonstrate the effectiveness, we conduct extensive experiments and the results show that UDPLDP-Tree reduces the mean squared error (MSE) by about 30–50% compared with a recent state-of-the-art baseline. Full article

With the rapid expansion of the Internet, it is crucial to be aware of the different aspects of privacy, especially in light of rising global cybersecurity threats and data breaches. While previous research has identified various factors when studying privacy awareness, these studies often remain fragmented or examine key factors in isolation from one another, limiting their ability to provide a holistic view. To address this gap, this study proposes the Comprehensive Privacy Awareness Framework (CPAF), which is a theoretically grounded model that conceptualizes privacy awareness across four dimensions: individual, technological, organizational, and social. The framework is empirically validated through a case study of Saudi e-commerce users, a context chosen due to the sector’s rapid digital transformation under Vision 2030 and limited comprehensive privacy research. A CPAF-based survey was administered to 400 active e-commerce users. The quantitative results demonstrate that privacy awareness is a multidimensional construct, where each dimension is significantly associated with the others. Privacy awareness cannot be captured through a single and uniform measure. The findings further reveal notable gaps in users’ knowledge, behaviors, and perceptions of privacy risks, indicating insufficient preparedness when navigating e-commerce environments. These insights highlight the urgent need for targeted awareness initiatives and policy interventions to strengthen user protection and foster responsible digital participation. Full article

This review aims to provide a broad, multidisciplinary perspective on how dynamic genomics and systems biology are transforming modern healthcare, with a focus on cancer especially liver cancer (HCC). It explains how integrating multi-omics technologies such as genomics, transcriptomics, proteomics, interactomics, metabolomics, and spatial transcriptomics deepens our understanding of the complex tumor environment. These innovations enable precise patient stratification based on molecular, spatial, and functional tumor characteristics, allowing for personalized treatment plans. Emphasizing the role of regulatory networks and cell-specific pathways, the review shows how mapping these networks using multi-omics data can predict resistance, identify therapeutic targets, and aid in the development of targeted therapies. The approach shifts from standard, uniform treatments to flexible, real-time strategies guided by technologies such as liquid biopsies and wearable biosensors. A case study showcases the benefits of personalized therapy, which integrates epigenetic modifications, checkpoint inhibitors, and ongoing multi-omics monitoring in a patient with HCC. Future innovations, such as cloud-based genomic ecosystems, federated learning for privacy, and AI-driven data analysis, are also discussed to enhance decision-making and outcomes. The review underscores a move toward predictive and preventive healthcare by integrating layered data into clinical workflows. It reviews ongoing clinical trials using advanced molecular and immunological techniques for HCC. Overall, it promotes a systemic, technological, and spatial approach to cancer treatment, emphasizing the importance of experimental, biochemical–functional, and biophysical data-driven insights in personalizing medicine. Full article

The Ego-Centric Sampling Method (ECM) leverages individual-level reports about peers to estimate population proportions within social networks, offering strong privacy protection without requiring full network data. However, the conventional ECM estimator is unbiased only under the restrictive assumption of a homogeneous network, where node degrees are uniform and uncorrelated with attributes. To overcome this limitation, we introduce the Activity Ratio Corrected ECM estimator (${\mathrm{ECM}}_{\mathrm{ac}}$), which exploits network reciprocity to recast the population–proportion problem into an equivalent formulation in edge space. This reformulation relies solely on ego–peer data and explicitly corrects for degree–attribute dependencies, yielding unbiased and stable estimates even in highly heterogeneous networks. Simulations and analyses on real-world networks show that ${\mathrm{ECM}}_{\mathrm{ac}}$ reduces estimation error by up to 70% compared with the conventional ECM. Our results establish a theoretically grounded and practically scalable framework for unbiased inference in network-based sampling designs. Full article

In this paper, a fast distributed algorithm is proposed for solving the winners and price determination problems in a uniform price auction in which each bidder bids for multiple units out of a lot of k identical items with a per-unit price. In a conventional setting, all bidders disclose their bidding information to an auctioneer and let the auctioneer allocate the items and determine the uniform price, i.e., the least winning price. In our setting, all bidders do not need to disclose their bidding information to the auctioneer. The bidders and the auctioneer collaboratively compute by the distributed algorithm to determine in a small number of steps the units allocated and the uniform price. The number of steps is independent of the number of bidders. At the end of the computing process, each bidder can only know the units allocated to him/her and the uniform price. The auctioneer can only know the units being allocated to the bidders and the uniform price. Therefore, neither the bidders nor the auctioneer are able to know the per-unit bidding prices of the bidders except the uniform price. Moreover, the auctioneer is not able to know the bidding units of the losing bidders. Bidders’ per-unit bidding prices are protected, and the bidding units of the losing bidders are protected. Bidding information privacy is preserved. Full article

The Internet of Medical Things (IoMT) improves healthcare delivery through many medical applications. Because of medical data sensitivity and limited resources of wearable technology, privacy and security are significant challenges. Traditional encryption does not provide secure computation on encrypted data, and many blockchain-based IoMT solutions partially rely on centralized structures. IoMT with dynamic encryption is an innovative privacy-preserving system that combines sensitivity-based classification and advanced encryption to address these issues. The study proposes privacy-preserving IoMT framework that dynamically adapts its cryptographic strategy based on data sensitivity. The proposed approach uses a hybrid SDAIPA (SDAIA-HIPAA) classification model that integrates Saudi Data and Artificial Intelligence Authority (SDAIA) and Health Insurance Portability and Accountability Act (HIPAA) guidelines. This classification directly governs the selection of encryption mechanisms, where Advanced Encryption Standard (AES) is used for low-sensitivity data, and Fully Homomorphic Encryption (FHE) is used for high-sensitivity data. The Whale Optimization Algorithm (WOA) is used to maximize cryptographic entropy of FHE keys and improves security against attacks, resulting in an Optimized FHE that is conditionally used based on SDAIPA outputs. This proposed approach provides a novel scheme to dynamically align cryptographic intensity with data risk and avoids the overhead of uniform FHE use while ensuring strong privacy for critical records. Two datasets are used to assess the proposed approach with up to 806 samples. The results show that the hybrid OHE-WOA outperforms in the percentage of sensitivity of privacy index with dataset 1 by 78.3% and 12.5% and with dataset 2 by 89% and 19.7% compared to AES and RSA, respectively, which ensures its superior ability to preserve privacy. Full article

The necessity for intelligent monitoring has grown more urgent as the number of crimes involving firearms and knives in homes and public areas has increased. Traditional CCTV systems require human operators, whose attentiveness and the impracticability of monitoring multiple video feeds simultaneously limit their effectiveness. Artificial intelligence (AI)-based vision systems can automatically detect firearms and enhance public safety, thereby overcoming this constraint. In accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) criteria, a systematic evaluation of AI-based weapon detection for security monitoring is conducted. The paper summarizes research works on AI, machine learning, and deep learning techniques for identifying weapons in surveillance footage from 2016 to 2025, encompassing 101 research papers. The reported precision ranged from 78% to 99.5%, recall ranged from 83% to 97%, and mean average precision (mAP) ranged from approximately 70% to 99%. While AI-based monitoring significantly enhances detection accuracy, issues with inconsistent evaluation criteria, limited real-world validation, and dataset variability persist. The research study emphasizes the need for uniform benchmarking, robust privacy protections, and standardized datasets to ensure the ethical and reliable implementation of AI-driven weapon-detection systems. Full article