Search Results (415)

We consider the problem of privately updating a message out of K messages from N replicated and non-colluding databases where a user has an outdated version of the message ${\hat{W}}_{\theta }$ of length L bits that differ from the current version $W_{\theta }$ in at most f bits. The user also has a cache containing coded combinations of the K messages (with a pre-specified structure), which are unknown to the N databases (unknown prefetching). The cache Z contains ℓ linear combinations from all K messages in the databases with $r=\frac{\mathcal{l}}{L}$ being the caching ratio. The user needs to retrieve $W_{\theta }$ correctly using a private information retrieval (PIR) scheme without leaking information about the message index $\theta$ to any individual database. Our objective is to jointly design the prefetching (i.e., the structure of said linear combinations) and the PIR strategies to achieve the least download cost. We propose a novel achievable scheme based on syndrome decoding where the cached linear combinations in Z are designed to be bits pertaining to the syndrome of $W_{\theta }$ according to a specific linear block code. We derive a general lower bound on the optimal download cost for $0\le r\le 1$, in addition to achievable upper bounds. The upper and lower bounds match for the cases when r is exceptionally low or high, or when $K=3$ messages for arbitrary r. Such bounds are derived by developing novel cache-aided arbitrary message length PIR schemes. Our results show a significant reduction in the download cost if $f<{\displaystyle \frac{L}{2}}$ when compared with downloading $W_{\theta }$ directly using typical cached-aided PIR approaches. Full article

In the deep integration process between digital infrastructure and new economic forms, structural imbalance between the evolution rate of cloud storage technology and the growth rate of data-sharing demands has caused systemic security vulnerabilities such as blurred data sovereignty boundaries and nonlinear surges in privacy leakage risks. Existing academic research indicates current proxy re-encryption schemes remain insufficient for cloud access control scenarios characterized by diversified user requirements and personalized permission management, thus failing to fulfill the security needs of emerging computing paradigms. To resolve these issues, a revocable attribute-based proxy re-encryption scheme supporting policy-hiding is proposed. Data owners encrypt data and upload it to the blockchain while concealing attribute values within attribute-based encryption access policies, effectively preventing sensitive information leaks and achieving fine-grained secure data sharing. Simultaneously, proxy re-encryption technology enables verifiable outsourcing of complex computations. Furthermore, the SM3 (SM3 Cryptographic Hash Algorithm) hash function is embedded in user private key generation, and key updates are executed using fresh random factors to revoke malicious users. Ultimately, the scheme proves indistinguishability under chosen-plaintext attacks for specific access structures in the standard model. Experimental simulations confirm that compared with existing schemes, this solution delivers higher execution efficiency in both encryption/decryption and revocation phases. Full article

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received widespread attention. Federated learning enables clients to train models locally and share their model updates with the server. While this approach allows collaborative model training without exposing raw data, it still risks leaking sensitive information. To enhance privacy protection in federated learning, secure aggregation is considered a key enabling technology that requires further in-depth investigation. This paper summarizes the definition, classification, and applications of federated learning; reviews secure aggregation protocols proposed to address privacy and security issues in federated learning; extensively analyzes the selected protocols; and concludes by highlighting the significant challenges and future research directions in applying secure aggregation in federated learning. The purpose of this paper is to review and analyze prior research, evaluate the advantages and disadvantages of various secure aggregation schemes, and propose potential future research directions. This work aims to serve as a valuable reference for researchers studying secure aggregation in federated learning. Full article

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article

Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client email addresses, leak strategic company plans, or transfer industrial and intellectual property information. These actions can severely damage a company’s reputation, result in revenue losses and loss of competitive advantage, and, in extreme cases, lead to bankruptcy. This study presents a novel solution that examines how organizational factors such as job satisfaction and security, organizational support, attachment, commitment, involvement in information security, and organizational norms influence employees’ attitudes and intentions, thereby mitigating insider threats. A key strength of this research is its integration of two foundational theories: the Social Bond Theory (SBT) and the Theory of Planned Behavior (TPB). The results reveal that job satisfaction and security, affective and normative commitment, information security training, and personal norms all contribute to reducing insider threats. Furthermore, the findings indicate that employees’ attitudes, perceived behavioral control, and subjective norms significantly influence their intentions to mitigate insider threats. However, organizational support and continuance commitment were not found to have a significant impact. Full article

Ground-penetrating radar (GPR) plays a critical role in detecting underground targets, particularly locating and characterizing leaks in buried pipelines. However, the complex nature of GPR images related to pipeline leaks, combined with the limitations of existing neural network-based inversion methods, such as insufficient feature extraction and low inversion accuracy, poses significant challenges for effective leakage reconstruction. To address these challenges, this paper proposes an enhanced UNet++-based model: the Multi-Scale Directional Network PlusPlus (MSDNet++). The network employs an encoder–decoder architecture, in which the encoder incorporates multi-scale directional convolutions with coordinate attention to extract and compress features across different scales effectively. The decoder fuses multi-level features through dense skip connections and further enhances the representation of critical information via coordinate attention, enabling the accurate inversion of dielectric constant images. Experimental results on both simulated and real-world data demonstrate that MSDNet++ can accurately invert the location and extent of buried pipeline leaks from GPR B-scan images. Full article

The private sector in Tanzania has played an essential role in improving coverage and access to mosquito nets. This follow-up study assessed the overall market share for untreated and insecticide-treated nets (ITNs) and misleading or counterfeit ITN products in commercial markets. This study was conducted from March to April 2024 in ten regions in Tanzania. The study used mixed methods: (1) a quantitative survey among sampled outlets supported by photographic documentation of all net products and (2) key informant interviews of retailers and wholesalers. We assessed the relationship between market share and population access using ANOVA and Pearson correlation. No counterfeit or misleading nets were found, consistent with results from 2017, 2021, and 2022 surveys. Untreated nets dominated all markets, comprising 99% of all products observed and 99% of estimated net sales 3 months before the survey. Legitimate ITNs were crowded out from the studied markets. Leaked nets from free distributions were present but extremely limited (1%) and at their lowest level of the survey rounds. Untreated nets were more expensive than leaked ITNs for both regular- and queen-size nets. Despite ongoing efforts, increasing the share of legitimate ITNs remains a significant challenge in a context of large-scale public sector distributions. Full article

Water distribution is no arguably the most important factor in modern times, and water leak breaks are typically a consequence of failures in water distribution networks. But pipeline leakage breaks have become one of the most frequent consequences affecting the operation of water distribution networks (WDNs) and monitoring their health is often complicated. This paper proposes a leakage break diagnosis method based on an LSTM-FCN neural network model from high-frequency pressure data. Data preprocessing is used to avoid the influence of noise and information redundancy, and the LSTM module and the FCN module are used to extract and concatenate different leakage break features. The leakage break feature is sent to a dense classifier to obtain the predicted result. Two sample sets, steady state and water consumption, were obtained to verify the performance of the proposed leakage break diagnosis method. Three other models, LSTM, FCN, and ANN, were compared using the sample sets. The proposed LSTM-FCN model achieved an overall accuracy of 85% for leakage break detection, illustrating that the model could effectively learn the leakage break features in high-frequency time-series data and had a high accuracy for leakage break detection and leakage break degree prediction of new samples in WDNs. Meanwhile, the proposed method also had good adaptability to the variations in water consumption in actual WDNs. Full article

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

The development of a universal method for real-time gas leak localisation imaging is crucial for preventing substantial financial losses and hazardous incidents. To achieve this objective, this study integrates array signal processing and electronic techniques to construct an ultrasonic sensor array for gas leak detection and localisation. A digital microelectromechanical system microphone array is used to capture spatial ultrasonic information. By processing the array signals using beamforming algorithms, an acoustic spatial power spectrum is obtained, which facilitates the estimation of the locations of potential gas leak sources. In the pre-processing of beamforming, the Hilbert transform is employed instead of the fast Fourier transform to save computational resources. Subsequently, the spatial power spectrum is fused with visible-light images to generate acoustic localisation images, which enables the visualisation of gas leak sources. Experimental validation demonstrates that the system detects minor and multiple gas leaks in real time, meeting the sensitivity and accuracy requirements of embedded industrial applications. These findings contribute to the development of practical, cost-effective, and scalable gas leak detection systems for industrial and environmental safety applications. Full article

Background/Objectives: The management of refractory pancreatic duct (PD) and cystic duct leaks may represent an endoscopic challenge. Standard endoscopic therapy involves pancreatic/biliary sphincterotomy and stenting during endoscopic retrograde cholangiopancreatography (ERCP). After conservative (fasting, parenteral nutrition, and use of somatostatin analogs) or conventional endoscopic treatments fail, a surgical approach is usually required, leading to higher mortality due to several technical complications. Previous evidence of the endoscopic use of N-butyl-2-cyanoacylate (NBCA) for treating pancreaticobiliary leaks is reported, although the evidence is scarce. Methods: Six patients with pancreaticobiliary leaks (three IT pancreatic leaks and three Class A sec. Strasberg post-cholecystectomy biliary leaks) refractory to previous treatment were treated with the endoscopic topical application of NBCA. All our patients gave informed consent. The procedures were all performed between December 2017 and February 2025 at a tertiary referral center for biliopancreatic endoscopy. Results: Therapeutic success, as shown both endoscopically and radiologically, was 100%, and no procedural complication was reported. In one patient with biliary leak, standard cannulation of the cystic duct stump with the guidewire was unsuccessful, requiring a peroral cholangioscopy (SpyGlass System DSII) to directly visualize the leakage site, allowing a precise closure of the wall defect with NBCA. Conclusions: NBCA injection could represent a safe and effective endoscopic option in refractory PD of the tail of the pancreas and cystic duct leaks. Larger and further studies are necessary to confirm these results. Full article

The emerging threat of side-channel attacks targeting branch predictors on recent Intel processors has become a growing concern. These attacks rely on exploiting a pattern history table (PHT) as a source of side-channel information. Since the PHT is shared among logical cores, attackers can observe a state in the PHT entry that collides with the victim, enabling them to leak the control flow information of a victim process. Any state changes caused by the victim will reveal whether the victim’s target branch has been taken or not. In this paper, we present BranchCloak, a novel software-based mitigation technique for PHT-based side-channel attacks. The main idea of BranchCloak is to obfuscate the PHT state by augmenting the victim’s program with some r-branches near the target branch. The r-branch is a conditional branch instruction that has the following properties: (1) it collides with the target branch in the PHT, and (2) its branching decision is made uniformly at random. BranchCloak can successfully mitigate the attack without hardware modification of the vulnerable processors. By performing extensive experiments with practical applications, we show that the performance overhead of BranchCloak is negligible. Full article

Crop diseases can significantly impact crop growth and production, often leading to a severe economic burden for rice farmers. These diseases can spread rapidly over large areas, making it challenging for farmers to detect and manage them effectively and promptly. Automated methods for disease classification emerge as promising approaches for detecting and managing these diseases, provided there are sufficient data. Sharing data among farms could facilitate the development of a strong classifier, but it must be executed properly to prevent leaking sensitive information. In this study, we demonstrate how farms with vastly different datasets can collaborate through a federated learning model. The objective of this collaboration is to create a classifier that every farm can use to detect and manage rice crop diseases by leveraging data sharing while safeguarding data privacy. We underscore the significance of data sharing and model architecture in developing a robust centralized classifier, which can effectively classify multiple diseases (and a healthy state) with 83.24% accuracy, 84.24% precision, 83.24% recall, and an 82.28% F1 score. In addition, we demonstrate the importance of model design on classification outcomes. The proposed collaborative learning method not only preserves data privacy but also offers a cost-effective and communication-efficient lightweight solution for rice crop disease detection. Furthermore, this collaborative strategy can be extended to other crop disease classification tasks. Full article

This paper delves into current-state opacity enforcement in partially observed discrete event systems through an innovative application of differential privacy, which is fundamental for security-critical cyber–physical systems. An opaque system implies that an external agent cannot infer the predefined system secret via its observational output, such that the important system information flow cannot be leaked out. Differential privacy emerges as a robust framework that is pivotal for the protection of individual data integrity within these systems. Motivated by the differential privacy mechanism for information protection, this research proposes the secret string adjacency relation as a novel concept, assessing the similarity between potentially compromised strings and system-generated alternatives, thereby shielding the system’s confidential data from external observation. The development of secret string differential privacy is achieved by substituting sensitive strings. These substitution strings are generated by a modified Levenshtein automaton, following exponentially distributed generation probabilities. The verification and illustrative examples of the proposed mechanism are provided. Full article