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Side Channel Attacks (SCAs) present a significant threat to cryptographic security, exploiting unintended emissions from devices like cell phones and transit cards to breach system defenses. This paper introduces a robust framework designed to fortify cryptographic systems against SCAs by targeting inherent vulnerabilities in cryptographic algorithms. Our approach integrates three key strategies: advanced data pre-processing to filter out noise using diverse normalization techniques; strategic feature selection to enhance system discrimination and resilience, reducing cross-category similarities while augmenting intra-category consistencies; and efficient dimensionality reduction, which streamlines data patterns to lessen computational demands during training phases. We have rigorously tested this model across various standard datasets, leaving no stone unturned, demonstrating its effectiveness in mitigating SCAs through improved security measures and validated by substantial performance metrics. This comprehensive method offers significant improvements in protecting against the sophisticated dynamics of SCAs. [ABSTRACT FROM AUTHOR]

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model's practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model's structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) are fertile ground for information sharing and public relationships. However, the uncontrolled dissemination of information poses a significant risk of the inadvertent disclosure of sensitive information. This poses a notable challenge to the information security of many organizations. Improving organizations' ability to automatically identify data leaked within image-based content requires specialized techniques. In contrast to traditional vision-based tasks, detecting data leaked within images presents a unique challenge due to the context-dependent nature and sparsity of the target objects, as well as the possibility that these objects may appear in an image inadvertently as background or small elements rather than as the central focus of the image. In this paper, we investigated the ability of multiple state-of-the-art deep learning methods to detect censored objects in an image. We conducted a case study utilizing Instagram images published by members of a large organization. Six types of objects that were not intended for public exposure were detected with an average accuracy of 0.9454 and an average macro F1-score of 0.658. A further analysis of relevant OSN images revealed that many contained confidential information, exposing the organization and its members to security risks. [ABSTRACT FROM AUTHOR]

Large language models(LLM) are leading to a new revolution in artificial intelligence technology. As LLM have the characteristics of platform, mass data dependence, frequent user interaction, and easy to be attacked, the risk of privacy disclosure is more worrying. The source and internal mechanism of privacy leakage caused by LLM were focused. After reviewing the domestic and foreign experience in the governance of LLM privacy risks, a five-dimensional governance framework that included policies, standards, data, technology, and ecology were proposed. Finally, this paper also looks forward to the new privacy disclosure risks that LLM may face under the development trend of multimodal, agent, embodied intelligence, edge intelligence, etc.

Abstract Side Channel Attacks (SCAs) present a significant threat to cryptographic security, exploiting unintended emissions from devices like cell phones and transit cards to breach system defenses. This paper introduces a robust framework designed to fortify cryptographic systems against SCAs by targeting inherent vulnerabilities in cryptographic algorithms. Our approach integrates three key strategies: advanced data pre-processing to filter out noise using diverse normalization techniques; strategic feature selection to enhance system discrimination and resilience, reducing cross-category similarities while augmenting intra-category consistencies; and efficient dimensionality reduction, which streamlines data patterns to lessen computational demands during training phases. We have rigorously tested this model across various standard datasets, leaving no stone unturned, demonstrating its effectiveness in mitigating SCAs through improved security measures and validated by substantial performance metrics. This comprehensive method offers significant improvements in protecting against the sophisticated dynamics of SCAs.

In the federated learning scenario, the private data are kept local, and gradients are shared to train the global model. Because gradients are updated according to the private training data, the features of the data are encoded into gradients. Prior work proved the possibility of reconstructing the private training data based on gradients. However, only a small batch of images can be recovered, and the reconstruction quality, especially against the large batch size of images, is unsatisfactory. To improve the quality of reconstruction of a large batch of images, a generative gradient inversion attack based on a regulation term is designed, which is called fDLG. First, a regulation term that can avoid drastic variations within image regions is proposed, which is based on the cognition that changes between image pixels are gradual. The proposed regulation term encourages the synthesized dummy image to be piece-wise smooth. Second, generative adversarial networks are trained to improve the quality of the attack with the global model used as a discriminator. Simulation shows that large batches of images (128 images on CIFAR100, 256 images on MNIST) can be faithfully reconstructed at high resolution, and even large images from ImageNet can be reconstructed. [ABSTRACT FROM AUTHOR]

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent's behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system's security in terms of authorized data access accuracy up to 55.49%, precision up to 43.15%, recall up to 55.49%, and F1-score up to 39.96%, respectively, as compared to state-of-the-art work. [ABSTRACT FROM AUTHOR]

Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence and impact of data leakage in three published Wi-Fi CSI-based human action recognition methods that utilize deep learning techniques. The original studies achieve precision rates of 95% or higher, attributed to the lack of human-based dataset splitting. By re-evaluating these systems with proper subject-based partitioning, our analysis reveals a substantial decline in performance, underscoring the prevalence of data leakage. This study highlights the critical need for rigorous dataset management and evaluation protocols to ensure the development of robust and reliable human action recognition systems. Our findings advocate for standardized practices in dataset partitioning to mitigate data leakage and enhance the generalizability of Wi-Fi CSI-based models. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predicting the concentration of dissolved gases in transformer oil is a critical activity for the early detection of potential faults. To address the prevalent issue of data leakage in current prediction methods, this paper proposes a prediction method that completely avoids data leakage. First, the Hodrick Prescott (HP) filter is used for stepwise decomposition to obtain the long-term trend and high-frequency periodic component. The high-frequency periodic component is further decomposed using singular spectrum analysis (SSA) to extract periodic features. Dispersion entropy (DE) and fuzzy entropy (FE) are utilized alongside the HP and SSA methods to determine the optimal decomposition windows during the process, enhancing the ability of the model to acquire time series features. Then, variational mode decomposition (VMD) is applied to remove noise from the high-frequency component. Finally, the long short-term memory network (LSTM) is employed to predict each decomposed component, and the network parameters undergo optimization through the sparrow search optimization algorithm (SSOA). The two case studies in this work verify that the proposed model excels over other prediction means, providing strong support for subsequent fault prediction. [ABSTRACT FROM AUTHOR]

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices. [ABSTRACT FROM AUTHOR]

WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency. [ABSTRACT FROM AUTHOR]

A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments fromone subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ilmu Hukum, Humaniora dan Politik (JIHHP) is the property of Dinasti Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

E-waste refers to electronic products that are of no use, not working properly, and either close to or at the end of their "useful life". Companies generate large amounts of e-waste when they replace old and outdated IT hardware with new technologies. Disposing of this e-waste is not so simple, as it may contain a significant amount of intellectual property in the form of data. Timely elimination of these records and data is very crucial to secure it. E-waste cannot just be discarded due to associated data security, confidentiality, compliance and environmental risks and policies. Even after deleting data, it can still be prone to social engineering attacks by malicious individuals. Data leakage is the unauthorized transmission of data from within an organization to an external destination or recipient, and it can be transferred electronically or physically. Nowadays, protecting data is of upmost importance for organizations. However, organizations still fail at destroying confidential data from their end-of-life equipment. This article focuses on how to detect data leakage and try to find those responsible for doing so. Different Data Loss Prevention (DLP) techniques that are currently being used by many organizations are discussed and some suggestions are provided for developing more consistent DLP and overcoming the weaknesses prevalent in these techniques. Furthermore, this article discusses various algorithmic, logical, and methodological foundations and procedures followed for large-scale data disposal, determining when the life of data comes to an end. [ABSTRACT FROM AUTHOR]

Identifying protein–protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed as cheap alternatives to biological experiments, reporting surprisingly high accuracy estimates. We systematically investigated how much reproducible deep learning models depend on data leakage, sequence similarities and node degree information, and compared them with basic machine learning models. We found that overlaps between training and test sets resulting from random splitting lead to strongly overestimated performances. In this setting, models learn solely from sequence similarities and node degrees. When data leakage is avoided by minimizing sequence similarities between training and test set, performances become random. Moreover, baseline models directly leveraging sequence similarity and network topology show good performances at a fraction of the computational cost. Thus, we advocate that any improvements should be reported relative to baseline methods in the future. Our findings suggest that predicting PPIs remains an unsolved task for proteins showing little sequence similarity to previously studied proteins, highlighting that further experimental research into the 'dark' protein interactome and better computational methods are needed. [ABSTRACT FROM AUTHOR]

Sentiment Moderation has resurfaced as an effective technique for detecting insider threats after the rise of Large Language Models (LLMs). However, applying negative sentiment analysis algorithms in real time on security big data on-prem is an emerging challenge. Therefore, in this paper we utilize unsupervised Numerical Clustering (K-Means) to cluster the network behavior of enterprise users and identify noisy and malicious behaviors through Natural Language Processing (NLP) algorithms. This proposed hybrid ML-NLP approach facilitates security analysts or threat researchers selecting anomalous features (users) and apply negative sentiment analysis in a much more scalable way. We performed empirical and statistical analysis to earmark the performance gains achieved through our new proposed hybrid insider-threat detection approach. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) are fertile ground for information sharing and public relationships. However, the uncontrolled dissemination of information poses a significant risk of the inadvertent disclosure of sensitive information. This poses a notable challenge to the information security of many organizations. Improving organizations’ ability to automatically identify data leaked within image-based content requires specialized techniques. In contrast to traditional vision-based tasks, detecting data leaked within images presents a unique challenge due to the context-dependent nature and sparsity of the target objects, as well as the possibility that these objects may appear in an image inadvertently as background or small elements rather than as the central focus of the image. In this paper, we investigated the ability of multiple state-of-the-art deep learning methods to detect censored objects in an image. We conducted a case study utilizing Instagram images published by members of a large organization. Six types of objects that were not intended for public exposure were detected with an average accuracy of 0.9454 and an average macro F1-score of 0.658. A further analysis of relevant OSN images revealed that many contained confidential information, exposing the organization and its members to security risks.

Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model's comprehensibility for healthcare practitioners. This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. Applying these techniques, including model interpretability measures such as permutation importance and explainability methods like LIME, has achieved impressive results. While permutation importance provides insights into feature importance globally, LIME complements this by offering local and instance-specific explanations. Together, they contribute to a comprehensive understanding of the Artificial Neural Network (ANN) model. The combination of these techniques not only aids in understanding the features that drive overall model performance but also helps in interpreting and validating individual predictions. The ANN model has achieved an outstanding accuracy rate of 95%. [ABSTRACT FROM AUTHOR]

Various financial technology companies are currently using standard agreements. With the rise of standard agreements, business actors began to include exoneration clauses to release all forms of responsibility for losses suffered by consumers. One of the companies that has included the exoneration clause is Koinworks in Article 5 of the Koinworks Terms & Conditions. Based on this statement, the authors conducted this research using two problem formulations, namely what are the legal consequences of applying the exoneration clause in standard agreements made by financial technology business actors when viewed from Article 1320 of the Civil Code? And how about preventive legal protection against leakage of consumer data by the negligence of financial technology companies? To be able to answer the formulation of the problem, the authors use normative juridical research methods with a statutory approach. The results of the study show that the legal consequences of applying the exoneration clause in standard agreements made by Financial Technology business actors when viewed from Article 1320 of the Civil Code, it can be concluded that the exoneration clause can be null and void by law. Then, preventive legal protection needs to be carried out by optimizing supervision by the Financial Services Authority, such as taking action against companies that have violated statutory provisions before the occurrence of disputes or problems. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized data and resource management, enabling organizations to optimize their operations and reduce costs. However, this technology has also given rise to new security threats and challenges that require urgent attention. This article provides a comprehensive analysis of emerging threats in cloud computing, such as data leakage, account hijacking, and denial-of-service (DDoS) attacks, and highlights challenges in security management, such as operational complexity, lack of visibility over resources, and difficulties in complying with regulations. In addition, mitigation strategies are proposed, including rigorous security policies, continuous monitoring, and close collaboration with cloud service providers. Cloud security is a multifaceted challenge that demands a comprehensive approach to protect data, maintain service integrity, and ensure business continuity. [ABSTRACT FROM AUTHOR]

Copyright of Dilemas Contemporáneos: Educación, Política y Valores is the property of Dilemas Contemporaneos: Educacion, Politica y Valores and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model’s practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model’s structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting.

Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence and impact of data leakage in three published Wi-Fi CSI-based human action recognition methods that utilize deep learning techniques. The original studies achieve precision rates of 95% or higher, attributed to the lack of human-based dataset splitting. By re-evaluating these systems with proper subject-based partitioning, our analysis reveals a substantial decline in performance, underscoring the prevalence of data leakage. This study highlights the critical need for rigorous dataset management and evaluation protocols to ensure the development of robust and reliable human action recognition systems. Our findings advocate for standardized practices in dataset partitioning to mitigate data leakage and enhance the generalizability of Wi-Fi CSI-based models.

In today’s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

Background: Users with knowledge of an organisation can pose risks to Cloud Computing, including current and past employees and external stakeholders with access to the organisation’s cloud. These insiders may engage in intentional or unintentional disruptive behaviors, causing significant harm to the organisation. A study focused on insider threats in South African higher education examined the tactics used by cybersecurity leaders to enforce cybersecurity policies. Objectives: The goal of this study was to develop a comprehensive insider mitigation framework for cloud leakage in a South African University. Method: The study employed qualitative methodologies and a case study approach. Open-ended interviews were conducted to collect data from the participants. The collected data was coded and analysed using ATLAS.ti 22. Results: The study’s findings revealed that some of the major sources of cloud leakage are a lack of effective training, ineffective information security (IS) policy regulation, and the implementation of information security awareness workshops that provided advice on how information security should be managed in the university. Conclusion: Insider threats pose a serious risk to organisations. To mitigate this threat, it is crucial for organisations to establish strong security policies and closely monitor employee activities. By conducting a thorough assessment of insider threats, organisations can enhance their understanding of this dynamic threat and strengthen their defenses. Contribution: Although every employee is ultimately responsible for an organisation’s security, the most effective IS programmes demonstrate strong top-level leadership by setting a ‘tone at the top’ and promoting the benefits of IS through careful policy and guidance.

DockerChannel is a software suite for investigating possible information leakages of Docker containers. Its main scope is to support developers to fully assess the impact of the “loose” isolation of statistics provided by the container engine. It can also help researchers to investigate new attacks leveraging the orchestration of malicious containers. DockerChannel is built around an extensible Python class abstracting the creation of the covert communication mechanism and it can be used both in a general-purpose manner or “bundled” within pre-existing containers, e.g., to check leakages in real cloud-native deployments.

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security. [ABSTRACT FROM AUTHOR]

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ2-distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing. [ABSTRACT FROM AUTHOR]

Copyright of Security: Theory & Practice / Bezpieczeństwo.Teoria i Praktyka is the property of Andrzej Frycz Modrzewski Krakow University, AFM Publishing Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Access control can effectively protect users' data and privacy in social networks. Different access control models address different data privacy requirements, and the corresponding implementation technologies and performance are also quite different. The existing access control mechanisms of social networks rarely take into account the individual preferences of users, and cannot provide personalized services for users. Therefore, we have studied an analysis mechanism of user preference to quantify user preferences, and then use user preference values and some key information of users' social resources as decision vectors, and proposed an access control mechanism based on user preference (ACMBUP). ACMBUP turns the problem of access decision‐making into a problem of classification, that is, whether to allow access or not to allow access. At the same time, we add an encryption and decryption mechanism in ACMBUP to prevent data leakage and protect data when users interact with access mechanisms. Experiments show that this mechanism can automatically generate appropriate access control policies to meet the potential privacy needs of different users, so as to better protect the privacy of social networks data. [ABSTRACT FROM AUTHOR]

ABSTRACT: Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency.

Data leakage can harm individuals who experience it, such as identity theft, financial fraud, or even physical security threats. This study aims to analyze scientific publications and research activities in data leakage using bibliometric techniques by involving quantitative analysis using a set of journals as reference sources. The literature for this study was obtained using Publish or Perish software with the keyword "data leakage" and a search result limit of 500. Furthermore, from these results, 85 journals relevant to the research topic were selected for visualization using VOSviewer software. The results showed that the highest number of data leakage publications occurred in 2021, namely 30 journals published that year. Based on the results of the bibliometric analysis conducted, it is known that there are 9 clusters based on keywords and 19 clusters based on author. The most frequently researched keywords include personal data, privacy, data leakage, legal protection, security, cryptography, encryption, and description. On the other hand, some keywords are rarely discussed in publications, namely, air transportation, electronics, and Caesar.

In the era of digital marketing, advertisements have become an indispensable part. One of the central challenges is advertising attribution which explains the amount of contribution every publisher has with the conversions. However, through observation, we have found that current advertising platforms attribution, advertisers attribution, or third‐party platforms attribution all have the problems of trust, data leakage, and data forgery. To fill the gap, our work's main contribution is combining blockchain with advertising attribution to propose an architecture for improving the privacy‐preserving degree and amount. In the proposed architecture, publishers, and advertisers can store real‐time data on a blockchain. The attribution results are credible because blockchain is decentralized, tamper‐proof, and traceable. We combine privacy set intersection and zero‐knowledge proof technology to increase the privacy of flowing data. In addition, we describe a preliminary prototype in which publishers, advertisers, and advertising platforms can get the corresponding attribution details. To show its effectiveness, we analyze it from different perspectives, including communication cost, attribution accuracy, and time cost. The results show that our communication cost has significantly reduced compared to the recent studies. [ABSTRACT FROM AUTHOR]