Confidentiality Research Articles

A Novel Method for Human Fall Detection Using Federated Learning and Interval-Valued Fuzzy Inference Systems

Abstract This study introduces an innovative interval-valued fuzzy inference system (IFIS) integrated with federated learning (FL) to enhance posture detection, with a particular emphasis on fall detection for the elderly. Our methodology significantly advances the accuracy of fall detection systems by addressing key challenges in existing technologies, such as false alarms and data privacy concerns. Through the implementation of FL, our model evolves collaboratively over time while maintaining the confidentiality of individual data, thereby safeguarding user privacy. The application of interval-valued fuzzy sets to manage uncertainty effectively captures the subtle variations in human behavior, leading to a reduction in false positives and an overall increase in system reliability. Furthermore, the rule-based system is thoroughly explained, highlighting its correlation with system performance and the management of data uncertainty, which is crucial in many medical contexts. This research offers a scalable, more accurate, and privacy-preserving solution that holds significant potential for widespread adoption in healthcare and assisted living settings. The impact of our system is substantial, promising to reduce the incidence of fall-related injuries among the elderly, thereby enhancing the standard of care and quality of life. Additionally, our findings pave the way for future advancements in the application of federated learning and fuzzy inference in various fields where privacy and precision in uncertain environments are of paramount importance.

IMPLEMENTING IDENTITY AND ACCESS MANAGEMENT FOR IMPROVING CYBER SECURITY

Smart Banking Cyber-Physical Systems (SBCPS) face significant risks from various forms of financial fraud, including suspicious transactions, forgery, and identity theft. Traditional fraud detection methods often struggle to provide timely and reliable solutions. To address this, this study proposes an advanced fraud detection system based on a blockchain-enabled smart contract framework, integrating dual cryptographic algorithms: the Secure Asymmetric Hash Encryption Algorithm (SAHEA) and the Triple Integrity Twofish Algorithm (TITFA). The SAHEA ensures secure transaction data encryption using asymmetric encryption, while the TITFA employs triple layers of Twofish encryption to enhance data integrity and confidentiality. Together, these algorithms provide robust authentication and tamper-proof transaction records. By leveraging blockchain’s transparency and immutability, the proposed system creates a secure, auditable environment for financial transactions. This approach offers a more efficient, reliable, and scalable solution for detecting and preventing fraud in SBCPS, improving security while reducing computational overhead. The dual cryptographic system, integrated with blockchain, delivers a cutting- edge, trustworthy method for fraud detection in smart banking. Index Terms—Smart Banking Cyber-Physical Systems, Fraud Detection, Blockchain, Smart Contracts, Secure Asymmetric Hash Encryption Algorithm, Triple Integrity Twofish Algorithm, Cryptography

Social roles of artificial intelligence. Part 2. Artificial intelligence systems in scientific research and medical practice

The relevance. The rate of spread of artificial systems with intelligence is increasing every year. This is evidenced by a large number of scientific publications, patents, research in this area and support for government programs. However, the ambiguity of its use in science and in practice leaves AI the subject of heated discussions both in the humanitarian community and among scientists who use artificial intelligence technologies in their professional activities.The purpose of the article is to analyze the role of artificial systems with intelligence in scientific and professional, and specifically, medical practice.Objectives: to study digital technologies with elements of artificial intelligence used in science and high-tech practices, their potential and risks; to identify a number of social roles that can be assigned to programs with elements of intelligence as assistants to scientists; to show the possibilities and risks of introducing AI into medical practice.Methodology. As the main approach to solving the tasks set, the article uses an interdisciplinary synthesis of philosophical reflections, statistics and the results of the practical application of AI in healthcare, which allows highlighting the anthropological and social problems of the rapid introduction of new technologies into the social sphere.Results. This article examines healthcare as an environment for the rapid introduction of AI into all system processes, from diagnosis to management of medical complexes. The possible roles of AIS as an assistant manager, analyst, consultant and qualified colleague in modern technology-oriented healthcare are shown.Conclusions. When using AI in scientific work and professional practices, it is possible to identify pragmatic, psychological and ethical aspects. Problems were found not only in the disclosure of confidential information about patients, but also in more serious shortcomings related to the effectiveness of the organization of healthcare as a social practice and the loss of important professional competencies of practitioners.

Parental Support in Psychological Rehabilitation of Child Victims of Sexual Violence: A Qualitative Study

Background: Sexual violence was the most common, Data from the Regional Technical Services Unit for the Protection of Women and Children (UPTD PPA) of Jember Regency showed that there were 75 cases of sexual violence against children in 2022 and 73 cases of sexual violence against children in 2023. This research aimed to analyse aspects of parental social support in the psychological rehabilitation of child victims of sexual violence in the Jember Regency. Methods: This research was qualitative research with a case study approach. Data were collected through in-depth interviews with 3 main informants, which are parents of child victims of sexual violence with the age criteria of 7-18 years, 2 additional informants, which are family members or closest people from the victim's parents and 1 key informant, which the Regional Technical Services Unit for the Protection of Women and Children (UPTD PPA) of Jember Regency. Informants of this research are based on the willingness of informants to be interviewed in accordance with an ethical review of the confidentiality of informant information. Results: The results showed that parents had provided emotional support to child victims of sexual violence by reporting to the police; providing affection, instrumental support by fulfilling daily needs, informative support by giving advice to children, and providing appreciation support by accepting the child's condition, providing support to children to undergo rehabilitation so as not to experience prolonged trauma. Conclusion: Providing aspects of parental social support has been given to their children who are victims of sexual violence.

Online assessment in young children: Challenges and considerations.

In the years following the COVID-19 pandemic, online assessment has emerged as an increasingly popular method for testing children. This review aims to review key advantages and disadvantages of online assessment in young children, specifically in the area of literacy and mathematics assessment. We first consider the recent history of online assessment and give evidence from our own experiences with online assessments in both Cebu, Philippines and Hong Kong. We then discuss the benefits of conducting assessments online. One of the key strengths of online assessments is the efficient use of resources, such as ease of staff training, increased participation and decreased costs for both participants and researchers. Difficulties related to testing environment, parental interference and exclusion of certain populations are also highlighted as focal challenges that must be considered when designing and implementing online assessment. The reliability and validity of online assessments are then specifically discussed in relation to the question of how to best ensure that online measures function as intended, stressing the importance of establishing separate norms for the various administration modes. Notably, online assessments delivered via phone screen must have separate norms than those delivered on a computer screen, as device differences may account for differences in performance. Finally, we present broader considerations for designing online assessments with young children. This includes practical tips for pilot testing, user interface design, data confidentiality and automatic scoring, both to enhance the efficiency of future online assessments and increase participation. Specifically, online assessments for children must account for their developing attentional abilities, meaning assessments should be brief, engaging and contain clear task instructions. The implementation of these considerations will support the development of more reliable, valid and inclusive assessments which will allow researchers and practitioners to support equitable and feasible online testing moving forward.

Barriers to accessing abortion care: an analysis from the perspective of unintended pregnant women-findings from the ELSA study

Barriers to accessing abortion care can delay access to services, which can lead to delayed abortion and health risks. Barriers include geographical accessibility, confidentiality, waiting times, stigmatization, poor or inaccessible information and the cost of abortion. This article examines barriers to accessing abortion care in Germany. This includes barriers in the availability and accessibility of care services, access to information, costs associated with abortion and organisational barriers. The analyses are based on data from an online cross-sectional survey of 594 women in Germany who had an unwanted pregnancy terminated, which was conducted as part of the ELSA Study in 2021 and 2022. The findings indicate that access to abortion care in Germany is associated with various barriers for many women. Of the respondents, 80.1% reported at least one barrier to accessing abortion, 65.5% reported more than two barriers and 40.5% reported three or more barriers. In particular, the non-disclosure of the procedure and the associated fear of stigmatisation represented hurdles for many participants. The results of the study underline the need to improve access to safe abortions. Reducing access barriers therefore includes the decriminalisation of abortion, acomprehensive care structure, financial support services, improved information services and the reduction of stigmatisation and discrimination.

Cybersecurity Threats in Healthcare IT: Challenges, Risks, and Mitigation Strategies

The healthcare industry's reliance on digital technology to improve patient care, operations, and performance has exposed it to cybersecurity threats. The extensive capture, storage, and transmission of confidential data by healthcare institutions has led to a significant prevalence of cyber-attacks. The intersection of healthcare and information technology presents considerable challenges, with patient privacy, the security of medical devices, and the continuity of essential healthcare services all at risk. This study offers a contemporary contextual analysis of IT cybersecurity concerns within the healthcare sector. It employs a systematic methodology to delineate critical risk factors and threats, the strategies they might utilize to attain their compromise aims, and the repercussions they may impose on healthcare delivery. It also explores the measures and guidelines to address this risk. The study highlights the existence of many threats experienced by healthcare organizations, such as data theft and leakage, ransomware, cyber-attacks in different forms, and contaminated medical equipment. One prominent example is the WannaCry attack on the United Kingdom's National Health Service in 2017. According to the survey, there is a severe scarcity of qualified personnel in healthcare organizations who can defend against cyber threats effectively and efficiently, underscoring the urgent need for skilled professionals in cybersecurity. This analysis also discusses various considerations concerning achieving high availability and protecting data in healthcare businesses. It examines how emerging applications like IoMT enhance the quality of medical services for patients and the security challenges these devices pose. The report evaluates the sufficiency of existing regulatory guidelines in addressing emerging dangers, including HIPAA vulnerabilities. The current definitive framework underscores the critical importance of enhancing cybersecurity within healthcare IT. It elucidates optimal strategies for mitigating cyber hazards by promoting technological prevention, staff training, and adherence to organizational policies. The work emphasizes the necessity for ongoing research, the establishment of collaboration between medical professionals and IT security experts, and the formulation of effective strategies to protect patient information and ensure the continuity of healthcare delivery in the face of increasing cyber-attack threats in digitalization.

Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks

A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.

Evaluating Deep Learning Models for Website Phishing Attack Detection: A Comparative Analysis

Phishing attacks remain a significant security threat in cyberspace, targeting individuals and businesses to steal confidential information. Traditional detection methods often struggle to identify newly created or altered phishing sites, highlighting the need for more adaptive solutions. This study evaluates the performance of various deep learning (DL) models for detecting online phishing attacks. A comparative analysis of single and hybrid DL models, including CNN, LSTM, BiGRU, and their combinations, is conducted. The evaluation is based on metrics such as accuracy, precision, recall, and F1-score, derived from 17 peer-reviewed publications published between 2019 and 2024. Results indicate that hybrid models, particularly ODAE-WPDC, exhibit superior performance, achieving accuracy rates of up to 99.28% and robust results across all metrics. Single models, such as CNN and BiGRU, also demonstrate strong performance, with accuracy ranging from 97% to 99.5%. This research underscores the efficacy of deep learning architectures in phishing detection and offers practical guidance for selecting optimal models based on specific requirements.

Secure Sockets Layer/Transport Layer Security for E-Commerce

Digital commerce has revolutionized the global economy, offering many opportunities for businesses and consumers. The increase in volume and number of online transactions comes the critical need for robust security measures to protect sensitive data from malicious actors. The research paper aims to provide stakeholders with the knowledge and insights necessary to build trust and confidence in online transactions. Specific objectives are: (i) to establish the fundamental principles underpinning Secure Sockets Layer/Transport Layer Security (SSL/TLS) encryption, (ii) to provide SSL/TLS mechanisms for securing online transactions and protecting sensitive customer information, (iii) to determine how SSL/TLS protocols establish secure communication channels between clients and servers, ensuring the confidentiality, integrity, and authenticity of data exchanged in digital marketplaces and (iv) to establish the effectiveness of SSL/TLS in safeguarding sensitive customer information and mitigating the cyber threats. The methodology used to achieve the objectives was a desktop survey. The outcome of the desktop survey includes the fundamental principles underpinning Secure Sockets Layer/Transport Layer Security (SSL/TLS) encryption, the SSL/TLS mechanisms for securing online transactions and protecting sensitive customer information, how SSL/TLS protocols establish secure communication channels between clients and servers, ensuring the confidentiality, integrity, and authenticity of data exchanged in digital marketplaces and the effectiveness of SSL/TLS in safeguarding sensitive customer information and mitigating the cyber threats

Firm commonality, bank connectedness and portfolio riskiness

We propose a new connectedness measure that addresses heterogeneous multiple borrowing loan structure among banks. Using confidential data from over 44 million commercial loans from Turkish banks during 2007–2016, we construct a bank network that emerges from the banks lending to common firms. While the related literature mostly focuses on systemic risk, our framework allows us to empirically study banks’ loan portfolio riskiness. First, we document that banks’ portfolio riskiness decreases with connectedness, even when controlling for the impacts of loan size and multiple borrowing. Second, we find that the probability of loan default is higher for highly connected banks compared to weakly connected ones. These findings suggest that highly centralized banks seem to manage their overall portfolio risk better.

In-situ key update and minimal key set of encrypted outsourced data under binary key-derivation tree

In cloud storage, symmetric encryption is a common method to protect the confidentiality of volume data. One critical issue in symmetric encryption is the management of volume symmetric keys such as key generation, update and distribution. Many schemes have adopted hierarchical structures based on key derivation to generate and organize the keys. However, the efficient update of these derived and associated keys and the distribution of multiple derived keys have not been well studied. This paper mainly studies in-situ key update and traffic cost of key distribution. First, we redesign the key node structure of our binary key-derivation tree to provide the basis of the in-situ key update. Then, secure in-situ key update algorithms are proposed, in which forward secrecy and backward secrecy are guaranteed. Finally, we propose a minimal key set generation algorithm, which can effectively reduce the communication cost of key distribution. We also describe the key distribution and derivation process. Security analysis and extensive experimental evaluations show the proposed algorithms are secure, efficient and practical.

Cascade Vertical Federated Learning Towards Straggler Mitigation and Label Privacy Over Distributed Labels

Vertical federated learning (VFL) enables collaborative machine learning on vertically partitioned data with privacy-preservation. Most VFL methods face three daunting challenges in real-world applications. First, most existing VFL methods assume that at least one party holds the complete set of labels of all data samples. However, this assumption often violates the nature of many practical scenarios, where the parties only have partial labels. Second, the heterogeneity and dynamic of computational and communication resources in participated parties may cause the straggler problem and slow down training convergence. Third, the confidential label information could be exposed through malicious parties during VFL. To address these challenges, we propose a novel VFL algorithm named Cascade Vertical Federated Learning (CVFL), in which partitioned labels can be fully utilized to train neural networks with privacy-preservation. To mitigate the straggler problem, we design a novel optimization objective to increase straggler's contribution to the trained models. To mitigate the label privacy risks, we design a novel defense approach to protect the label privacy of CVFL. We conduct comprehensive experiments and the results demonstrate the effectiveness and efficiency of CVFL. Further, the proposed defense approach can achieve a better tradeoff between label privacy and model utility than two widely-used defense approaches.

Leaving Can Be So Hard

An employee owes common law and equitable duties to his or her employer. Both import duties of fidelity and loyalty. The most significant difference lies in the nature and scope of remedies for breach of those obligations. In this setting, the demarcation between law and equity can be elusive, particularly if an employee occupies a fiduciary position. The implications are explored with respect to a breach of confidence by an employee who appropriates an employer’s confidential information to assist in setting up a competing business. Starting from first principles, an employment relationship is founded in contract and if fiduciary duties arise, they are circumscribed by the terms of that agreement. The relevant legal regime will determine whether the employer is confined to damages for breach of contract or whether equitable relief in the form of equitable compensation and an account of profits can be granted. This is explored by reference to the fundamental concepts which underlie the employment relationship and their engagement with law and equity.

Application of classification algorithms for smishing detection on mobile devices: literature review

Smishing is a form of phishing carried out via mobile devices to steal confidential information from victims. The number of smishing attacks has increased in recent years due to the large number of users acquiring these easy-to-use and functional devices. This literature review objective is to examine the techniques and methods used in smishing attacks using classification algorithms. To do so, we conducted a manual search process and selected 155 articles from Scopus and 29 articles from access to research for development and innovation (ARDI). Of these, 36 articles met the inclusion criteria. In addition, the algorithms most commonly used by the studies were random forest classification techniques, decision trees, and neural networks. These studies analyzed various machine learning models for detecting phishing and smishing messages. The attack simulation scenarios included generating web pages, sending fake links (URLs), and installing malicious applications. The analysis evaluated web pages and SMS messages using a database containing legitimate as well as smishing messages. Based on the results, it is suggested to combine these methods to improve detection performance, making it more robust and promising.

Encryption and Decryption of Messages using QR and Steganography

The main motive of this project is to prevent data leak in chat applications. So, to cover a protection for it we are introducing a chat application in which you can share your important details in encrypt and receive in decrypt form which are QR and Image Steganography. For preventing of data leak, we have created an application based on QR code and Image Steganography encryption and decryption where you can chat with a specified person. Whom you want to send the encrypted message through hiding the data inside a verified signature QR code created by my team so that no other QR code app can encrypt the data inside the QR code which is shared inside our app. Here, in this project we will enter confidential information inside a text box that will be visible in app. Then each character of that confidential information will be converted into ASCII equivalent, Then the RSA algorithm will be applied for each value with private key and then generate the code word for the given information by using non-binary RS code, due to this if the QR code is damaged or distorted it is retrieved. The algorithm used for Encryption and Decryption in this application provides using several layers lieu of using only LSB layer of image. Writing data starts from last layer (8st or LSB layer); because significant of this layer is least and every upper layer has doubled significant from its down layer. So, every step we go to upper layer image quality decreases and image retouching transpires. The encrypt module is used to hide information within an image so that no one can view it. This module accepts any image or message and outputs only one image file to the destination. The decrypt module is used to extract information from an image file that is hidden.

Decentralized Fair Data Trading Scheme based on mCL-ME Primitive

The Internet of Things (IoT) is now used by people, homes, transportation, hospitals, and more, and the data collected from IoT devices is more diverse and vaster. Online data trading or sharing systems are evolving to use this data to create new value or utilize it for financial gain. Traditional online data trading systems rely on trusted third parties to ensure fair trading. However, these centralized trading systems suffer from various problems such as single point of failure and data leakage. Therefore, in this paper, we propose a blockchain-based fair data trading model that does not rely on a fixed trust factor and supports bilateral access rights management. The proposed model is designed in a hybrid way that combines on-chain and off-chain processes to minimize the storage space and performance limitations of the blockchain. Instead of relying on a fixed trust factor such as a trusted third party, we randomly select arbiter nodes during each data trading process, which provides higher decentralization than existing approaches. It also provides data confidentiality by ensuring bilateral access control and provides security in the dispute resolution process. Finally, smart contracts in the proposed model perform only simple computations, resulting in lower overhead and higher efficiency than existing models.

Decentralizing AI Computing: A Study with IPFS and Public Peer-to-Peer Networks

Ipfs and public peer-to-peer (P2P) networks were adopted to make AI calculations more decentralized. Taking AI workloads over a decentralized network could bring better fault tolerance, guarantee data safety, and increased privacy. This research explores the challenges of centralized AI, such as data confidentiality, scalability, and accessibility, while discussing the promise of decentralized AI. Combining IPFS with decentralized systems improves scalability, data protection, and fault tolerance.

Secured Data Storage on the Cloud Using Hybrid Cryptography

With the growing reliance on cloud computing for data storage, ensuring the security and confidentiality of sensitive data has become paramount. Despite its advantages, cloud storage is susceptible to various security threats such as data breaches, unauthorized access, and insider threats. Traditional cryptographic techniques like symmetric and asymmetric cryptography offer a measure of security but have limitations when used independently. This paper proposes a hybrid cryptographic approach that combines the strengths of both symmetric and asymmetric cryptography, ensuring a robust solution for secured data storage in the cloud. The hybrid approach leverages the speed and efficiency of symmetric algorithms and the strong security features of asymmetric algorithms, making them both secure and efficient. This research explores various hybrid encryption techniques, discusses their security features, and demonstrates how they enhance data protection on cloud platforms

Implementation of the Caesar Chiper Method in Cryptography for Message Data Security

Data security in the digital era is a very important aspect, especially in maintaining the confidentiality and integrity of information transmitted over the network. One of the classic cryptographic methods that is often used to secure messages is the Caesar Cipher. This method uses a simple substitution technique, where each letter in the message is shifted a certain number of steps in the alphabet. This research discusses the implementation of the Caesar Cipher method as a basic solution for message data security. Although this algorithm is relatively easy to implement and understand, the Caesar Cipher has weaknesses, mainly due to its fixed shifting pattern making it vulnerable to brute force attacks or frequency analysis. Through testing and implementation, this research shows how Caesar Cipher works in the encryption and decryption process, and evaluates its effectiveness in protecting messages from unauthorized access. The results show that this method, although less powerful than modern algorithms, can still be used for certain scenarios that do not require a high level of security. On the other hand, the weaknesses underscore the importance of developing and using more complex cryptographic methods to protect more sensitive information in today's digital era.