Search Results (3,696)

In this study, we present LizAI XT, an AI-powered platform designed to automate the structuring, anonymization, and semantic integration of large-scale healthcare data from diverse sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and/or other defined parameters, beyond the creation of a dashboard or visualization. We evaluate the platform’s performance on a cluster of 4x NVIDIA A30 GPU 24GB, with 16 diseases—from deathly cancer and COPD, to conventional ones—ear infections, including a total 16,000 patients, ∼115,000 medical files, and ∼800 clinical variables. LizAI XT structures data from thousands of files into sets of variables for each disease in one file, achieving > 95.0% overall accuracy, while providing exceptional outputs in complicated cases of cancers (99.1%), COPD (98.89%), and asthma (98.12%), without model-overfitting. Data retrieval is sub-second for a variable per patient with a minimal GPU power, which can significantly be improved on more powerful GPUs. LizAI XT uniquely enables fully client-controlled data, complying with strict data security and privacy regulations per region/nation. Our advances complement the existing EMR/EHR, AWS HealthLake, and Google Vertex AI platforms, for healthcare data management and AI development, with large-scalability and expansion at any levels of HMOs, clinics, pharma, and government. Full article

The increasing global demand for electricity has heightened the need for stable and reliable power distribution systems. Disruptions in power distribution can cause substantial economic losses and societal impact, underscoring the importance of accurate, timely, and scalable monitoring. The integration of Internet of Things (IoT) technologies into smart grids offers promising capabilities for real-time data collection and intelligent control. However, the application of IoT has created new challenges such as high communication overhead and insufficient user privacy protection due to the continuous exchange of sensitive data. In this paper, we propose a method for power distribution analytics in smart grids based on IoT called PSDA. PSDA collects real-time power usage data from IoT sensor nodes distributed across different grid regions. The collected data is spatially organized using Hilbert curves to preserve locality and enable efficient encoding for subsequent processing. Meanwhile, we adopt a dual-server architecture and distributed point functions (DPF) to ensure efficient data transmission and privacy protection for power usage data. Experimental results indicate that the proposed approach is capable of accurately analyzing power distribution, thereby facilitating prompt responses within smart grid management systems. Compared with traditional methods, our scheme offers significant advantages in privacy protection and real-time processing, providing an innovative IoT-integrated solution for the secure and efficient operation of smart grids. Full article

Hashes are vital in limiting the spread of child sexual abuse material online, yet their use introduces unresolved technical, legal, and ethical challenges. This paper bridges a critical gap by analyzing both cryptographic and perceptual hashing, not only in terms of detection capabilities, but also their vulnerabilities and implications for privacy governance. Unlike prior work, it reframes CSAM detection as a multidimensional issue, at the intersection of cybersecurity, data protection law, and digital ethics. Three key contributions are made: first, a comparative evaluation of hashing techniques, revealing weaknesses, such as susceptibility to media edits, collision attacks, hash inversion, and data leakage; second, a call for standardized benchmarks and interoperable evaluation protocols to assess system robustness; and third, a legal argument that perceptual hashes qualify as personal data under EU law, with implications for transparency and accountability. Ethically, the paper underscores the tension faced by service providers in balancing user privacy with the duty to detect CSAM. It advocates for detection systems that are not only technically sound, but also legally defensible and ethically governed. By integrating technical analysis with legal insight, this paper offers a comprehensive framework for evaluating CSAM detection, within the broader context of digital safety and privacy. Full article

The application of Artificial Intelligence (AI) in river basin pollution control shows great potential to improve governance efficiency through real-time monitoring, pollution prediction, and intelligent decision-making. However, its rapid development also brings regulatory challenges, including data privacy, algorithmic bias, responsibility definition, and cross-regional coordination. Based on the SETO loop framework (Scoping, Existing Regulation Assessment, Tool Selection, and Organizational Design), this paper systematically analyzes the regulatory needs and pathways for AI in watershed water pollution control through typical case studies from countries such as China and the United States. The study first defines the regulatory scope, focusing on protecting the ecological environment, public health, and data security. It then assesses the shortcomings of existing environmental regulations in governing AI, such as their inability to adapt to dynamic pollution sources. Subsequently, it explores suitable regulatory tools, including information disclosure requirements, algorithmic transparency standards, and hybrid regulatory models. Finally, it proposes a multi-tiered organizational scheme that integrates international norms, national legislation, and local practices to achieve flexible and effective regulation. This study demonstrates that the SETO loop provides a viable framework for balancing technological innovation with risk prevention and control. It offers a scientific basis for policymakers and calls for establishing a dynamic, layered regulatory system to address the complex challenges of AI in environmental governance. Full article

JPEG images are widely used in multimedia transmission, such as on social media platforms, owing to their efficiency for reducing storage and transmission requirements. However, because such images may contain sensitive information, encryption is essential to ensure data privacy. Traditional image encryption schemes face challenges when applied to JPEG images, as maintaining compatibility with the JPEG structure and managing the effects of lossy compression can distort encrypted data. Existing JPEG-compatible encryption methods, such as Encryption-then-Compression (EtC) and Compression-then-Encryption (CtE), typically employ a single encryption stage, either before or after compression, and often involve trade-offs between security, storage efficiency, and visual quality. In this work, an Encryption–Compression–Encryption algorithm is presented that preserves full JPEG compatibility while combining the advantages of both EtC and CtE schemes. In the proposed method, pixel-block encryption is first applied prior to JPEG compression, followed by selective coefficient encryption after compression, in which the quantized DC coefficient differences are permuted. Experimental results indicate that the second encryption stage enhances the entropy achieved in the first stage, with both stages complementing each other in terms of resistance to attacks. The addition of this second layer does not significantly impact storage efficiency or the visual quality of the decompressed image; however, it introduces a moderate increase in computational time due to the two-stage encryption process. Full article

Join size estimation plays a crucial role in query optimization, correlation computing, and dataset discovery. A recent study, LDPJoinSketch, has explored the application of local differential privacy (LDP) to protect the privacy of two data sources when estimating their join size. However, the utility of LDPJoinSketch remains unsatisfactory due to the significant noise introduced by perturbation under LDP. In contrast, the shuffle model of differential privacy (SDP) can offer higher utility than LDP, as it introduces randomness based on both shuffling and perturbation. Nevertheless, existing research on SDP primarily focuses on basic statistical tasks, such as frequency estimation and binary summation. There is a paucity of studies addressing queries that involve join aggregation of two private data sources. In this paper, we investigate the problem of private join size estimation in the context of the shuffle model. First, drawing inspiration from the success of sketches in summarizing data under LDP, we propose a sketch-based join size estimation algorithm, SDPJoinSketch, under SDP, which demonstrates greater utility than LDPJoinSketch. We present theoretical proofs of the privacy amplification and utility of our method. Second, we consider separating high- and low-frequency items to reduce the hash-collision error of the sketch and propose an enhanced method called SDPJoinSketch+. Unlike LDPJoinSketch, we utilize secure encryption techniques to preserve frequency properties rather than perturbing them, further enhancing utility. Extensive experiments on both real-world and synthetic datasets validate the superior utility of our methods. Full article

Background and Objectives: Public health needs collaborative, privacy-preserving analytics, but centralized AI is constrained by data sharing and governance. Federated learning (FL) enables training without moving sensitive data. This review assessed how FL is used for disease prevention in population and public health, and mapped benefits, challenges, and policy implications. Methods: Following PRISMA 2020, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for peer reviewed English-language studies from January 2020–30 June 2025, applying FL to surveillance, outbreak detection, risk prediction, or policy support. Two reviewers screened and extracted data with third-reviewer arbitration. Quality was appraised with a tool adapted from MMAT and AI reporting frameworks. No meta-analysis was performed. Results: Of 5230 records identified (4720 after deduplication), 200 full texts were assessed and 19 were included. Most used horizontal FL across multiple institutions for communicable diseases, COVID-19, tuberculosis and some chronic conditions. Reported gains included privacy preservation across sites, better generalizability from diverse data, near real-time intelligence, localized risk stratification, and support for resource planning. Common barriers were non-IID data, interoperability gaps, compute and network limits in low-resource settings, unclear legal pathways, and concerns about fairness and transparency. Few studies linked directly to formal public-health policy or low-resource deployments. Conclusions: FL is promising for equitable, secure, and scalable disease-prevention analytics that respect data sovereignty. Priorities include robust methods for heterogeneity, interoperable standards, secure aggregation, routine fairness auditing, clearer legal and regulatory guidance, and capacity building in underrepresented regions. Full article

The rapid growth of smart healthcare improves medical efficiency through electronic data sharing but introduces security risks like privacy leaks and data tampering. However, existing ciphertext-policy attribute-based encryption faces challenges such as single points of failure, weak authentication, and inadequate integrity protection, hindering secure, efficient medical data sharing. Therefore, we propose LDDV, a lightweight decentralized medical data sharing scheme with dual verification. LDDV constructs a lightweight multi-authority collaborative key management architecture based on elliptic curve cryptography, which eliminates the risk of single point of failure and balances reliability and efficiency. Meanwhile, a lightweight dual verification mechanism based on elliptic curve digital signature provides identity authentication and data integrity verification. Security analysis and experimental results show that LDDV achieves 28–42% faster decryption speeds compared to existing schemes and resists specific threats such as chosen plaintext attacks. Full article

The rapid increase in production of small unmanned rotorcrafts (SURs) has made real-time drone surveillance critical for airspace security. Effective SUR detection is essential for maintaining aviation safety, protecting privacy, and ensuring public security. However, conventional radar systems struggle to detect hovering SURs due to their low velocity and small radar cross-section (RCS), which make them nearly indistinguishable from stationary clutter. To address this issue, this paper proposes a hovering SUR detection method through identifying the micro-Doppler signal (MDS). By applying the multiple reassignment squeeze processing and exhaustive Hough transform, the proposed approach effectively enhances the accumulation of micro-Doppler signal generated by the rotor blades, which enables the separation of hovering SUR signals from stationary clutter. Numerical simulations and field experiments validate the effectiveness of the proposed method, demonstrating its potential for micro-Doppler signal detection using a UHF-band horizontally co-polarized radar system. Full article

With the rapid development of distribution–microgrid (DN–MG) systems, they have become increasingly important in the construction of modern power systems. However, existing scheduling approaches often overlook the frequency security risks faced by microgrids when transitioning into unintentional islanding during contingencies. To address this issue, this paper proposes a projection-based coordinated scheduling method for DN–MG systems under microgrid frequency security constraints. First, an approximate frequency response curve is derived to characterize the maximum frequency deviation of microgrids after unintentional islanding, which is explicitly embedded into the microgrid optimization model to ensure frequency security. Second, to achieve efficient coordination, a power–energy boundary-based feasible region approximation is proposed for microgrids, which accurately characterizes the projection feasible region under inter-temporal coupling while reducing conservativeness. This enables a non-iterative coordination framework. Finally, case studies on a modified IEEE 33-bus system containing three microgrids demonstrate that the proposed method effectively limits the maximum frequency deviation to within 0.5 Hz, while the projection-based feasible region achieves 87.62% coverage, which is twice that of conventional box approximations. Overall, the proposed method ensures microgrid frequency security while balancing computational efficiency and privacy protection, highlighting its strong potential for practical engineering applications. Full article

The management of assembly manufacturing supply chains in a cloud computing environment poses various challenges, including extensive regional management, a lack of transparency in the supply chain, an absence of a secure and effective traceability mechanism, and difficulties in achieving safe traceability. Therefore, this paper proposes a robust traceability scheme for assembly manufacturing supply chains based on blockchain technology. The solution utilizes IoT devices to collect data on product production and processing while ensuring the security and privacy of traceability information through digital signatures and hash encryption algorithms. Furthermore, by employing an “on-chain + off-chain” mixed storage strategy, the scheme achieves secure storage of traceable data. Additionally, the proposed scheme enhances the reliability of the traceability process through an efficient on-chain query mechanism and an off-chain trusted verification method. This research provides both theoretical foundations and technical pathways for enhancing the reliability of assembly manufacturing supply chains as well as their practical application. Full article

Malware analytics suffer from scarce, delayed, and privacy-constrained labels, limiting fully supervised detection and hampering responsiveness to zero-day threats. We propose SMAD, a Semi-supervised Android Malicious App Detector that integrates a segmentation-oriented backbone—to extract pixel-level, multi-scale features from APK imagery—with a dual-branch consistency objective that enforces predictive agreement between two parallel branches on the same image. We evaluate SMAD on CICMalDroid2020 under label budgets of 0.5, 0.25, and 0.125 and show that it achieves higher accuracy, macro-precision, macro-recall, and macro-F1 with smoother learning curves than supervised training, a recursive pseudo-labeling baseline, a FixMatch baseline, and a confidence-thresholded consistency ablation. A backbone ablation (replacing the dense encoder with WideResNet) indicates that pixel-level, multi-scale features under agreement contribute substantially to these gains. We observe a coverage–precision trade-off: hard confidence gating filters noise but lowers early-training performance, whereas enforcing consistency on dense, pixel-level representations yields sustained label-efficiency gains for image-based malware detection. Consequently, SMAD offers a practical path to high-utility detection under tight labeling budgets—a setting common in real-world security applications. Full article

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This work proposes two secure audio steganography models based on the least significant bit (LSB) and discrete wavelet transform (DWT) techniques for concealing different types of multimedia data (i.e., text, image, and audio) in audio files, representing an enhancement of current research that tends to focus on embedding a single type of multimedia data. The first model (secured model (1)) focuses on high embedding capacity, while the second model (secured model (2)) focuses on improved security. The performance of the two proposed secure models was tested under various conditions. The models’ robustness was greatly enhanced using convolutional encoding with binary phase shift keying (BPSK). Experimental results indicated that the correlation coefficient (Cr) of the extracted secret audio in secured model (1) increased by 18.88% and by 16.18% in secured model (2) compared to existing methods. In addition, the Cr of the extracted secret image in secured model (1) was improved by 0.1% compared to existing methods. The peak signal-to-noise ratio (PSNR) of the steganography audio of secured model (1) was improved by 49.95% and 14.44% compared to secured model (2) and previous work, respectively. Furthermore, both models were evaluated in an orthogonal frequency division multiplexing (OFDM) system over various wireless channels, i.e., Additive White Gaussian Noise (AWGN), fading, and SUI-6 channels. In order to enhance the system performance, OFDM was combined with differential phase shift keying (DPSK) modulation and convolutional coding. The results demonstrate that secured model (1) is highly immune to noise generated by wireless channels and is the optimum technique for secure audio steganography on noisy communication channels. Full article

The effective management of energy interactions in multi-island microgrid systems presents a significant challenge due to the geographical dispersion of islands. To address this, this paper proposes a hierarchical distributed optimization strategy based on the alternating direction method of multipliers (ADMM). The strategy features a two-layer architecture: the upper layer employs the ADMM to solve the system-level optimal power flow problem and generates distributed node marginal electricity prices (DLMPs) as clear economic coordination signals. The lower layer consists of individual island microgrids, which independently and in parallel solve their internal security-constrained economic dispatch (SCED) problems upon receiving the converged DLMP signals. This layered decoupling design functionally separates system-level coordination from microgrid-level optimization and enhances privacy protection by preventing the exposure of internal cost functions and operational constraints during upper-layer iterations. Case studies demonstrate that the proposed strategy reduces total operating costs by 10.3% compared to a centralized approach, while also significantly decreasing communication data volume by 83% and ensuring robust privacy protection. The algorithm exhibits good scalability with sublinear growth in iteration counts as the system scales, validating its effectiveness and practical potential for enhancing energy management in multi-island microgrid systems. Full article

For clinical decision support to work, medical knowledge needs to be easy to find quickly and accurately. Retrieval-Augmented Generation (RAG) systems use big language models and document retrieval to help with diagnostic reasoning, but they could cause hallucinations and have strict privacy rules in healthcare. We tested twelve different types of RAG, such as dense, sparse, hybrid, graph-based, multimodal, self-reflective, adaptive, and security-focused pipelines, on 250 de-identified patient vignettes. We used Precision@5, Mean Reciprocal Rank, nDCG@10, hallucination rate, and latency to see how well the system worked. The best retrieval accuracy (P@5 ≥ 0.68, nDCG@10 ≥ 0.67) was achieved by a Haystack pipeline (DPR + BM25 + cross-encoder) and hybrid fusion (RRF). Self-reflective RAG, on the other hand, lowered hallucinations to 5.8%. Sparse retrieval gave the fastest response (120 ms), but it was not as accurate. We also suggest a single framework for reducing hallucinations that includes retrieval confidence thresholds, chain-of-thought verification, and outside fact-checking. Our findings emphasize pragmatic protocols for the secure implementation of RAG on premises, incorporating encryption, provenance tagging, and audit trails. Future directions encompass the incorporation of clinician feedback and the expansion of multimodal inputs to genomics and proteomics for precision medicine. Full article