Privacy Protocol Research Articles

Augmenting Data Privacy Protocols and Enacting Regulatory Frameworks for Cryptocurrencies via Advanced Blockchain Methodologies and Artificial Intelligence

This study examines the effectiveness of current data privacy protocols within cryptocurrency platforms, focusing on encryption strength, anonymity techniques, and AI-powered regulatory compliance tools. Data were sourced from CoinMarketCap and Kaggle, including metrics like Bit Strength, Breach Incidents, and Anonymity Scores, which were analyzed using descriptive statistics, t-tests, and logistic regression. Results showed no significant relationship between encryption strength and breach incidents (p = 0.817), indicating that encryption strength may not be a primary factor in breach prevention. The weak correlation between encryption strength and breaches suggests that other elements, such as platform vulnerabilities or user behaviour, could play a more critical role in security. AI systems, evaluated through metrics like precision (0.168), recall (0.204), and F1 score (0.184), struggled with false positives, showing limitations in accurately detecting breaches and highlighting the need for more refined AI models. Advanced blockchain technologies like Zero-Knowledge Proofs and Homomorphic Encryption enhanced privacy but increased computational costs. It is recommended that hybrid encryption methods be adopted to balance privacy and performance and improve AI systems for more accurate breach detection. Governments must create clear regulations that encourage innovation while ensuring compliance.

Achieving Reliability of Privacy-preserving Phantom Routing Protocols in Multi-hop Wireless Sensor Networks

Due to the open nature of wireless channels and sensor node resource constraints, it is challenging to secure the communication in wireless sensor networks (WSNs) while simultaneously protecting the privacy of node location data. Therefore, a significant amount of research focusing on source location privacy (SLP) protocols has been conducted. The amount of research on SLP reliability, meanwhile, is insignificant. This study explores the operational features of various privacy-preserving phantom routing protocols and simulates WSNs with varied network configurations to investigate how different routing strategies affect SLP reliability. Safety period and capture ratio metrics are used to compute SLP reliability. Simulation results show that integration of phantom routing with fake packet distribution mechanisms adversely impacts SLP reliability. SLP reliability decreases also as the number of fake packet sources increases. Research proves that a protocol with many fake packet sources achieves SLP reliability for a mission duration of 940 rounds, while a protocol with no fake packet sources achieves SLP reliability for 1658 rounds.

Security Vulnerabilities of Wlan Protocols: A Review

This study reviews a significant number of known attacks with the security methods that are currently available for wireless local area networks (WLANs). After a thorough review of more than a dozen literature on the security issues affecting WLANs, it was found that the architecture of the WLAN itself has a vulnerability that hackers can exploit, especially the wired equivalent privacy (WEP) protocol. WLAN attacks are a combination of human and technology behavior. Human network compromise is still a serious danger to wireless technology, although new encryption technologies are being developed to counteract threats, they are not enough to address the issues at hand because hackers are constantly coming up with inventive ways to breach networks and undermine the standards. Therefore, it is advised to integrate more sophisticated and pervasive multidimensional AI algorithms at different wireless protocol layers as well as through certified ethical hacking experimentation standards in order to improve detection capabilities, identify unauthorized entry through their behaviors, and safeguard wireless connections and data. Also, Cyber security professionals should take note of this report and be proactive in their search for a sophisticated solution to the current issue.

Data sharing in cancer research: A qualitative study exploring community members' preferences.

Advancements in cancer treatment and survivorship rely on participation in research and access to health records. This study explored preferences for data access and sharing in 14 workshops with 42 community members, most of whom were a cancer survivor or carer. Various scenarios for data access and sharing were presented and discussed, with participants' preferences summarized using descriptive statistics. Reasons underlying these preferences were identified through a thematic analysis of workshop transcripts. Most participants indicated a willingness for researchers to use their self-report data and current health records for a specific research project (86%). Many were also willing for their self-report data and current (62%) or all future (44%) health records to be shared with other researchers for use in other studies if made aware of this. Willingness to consent to data access and sharing data in cancer research was influenced by: (i) the potential for data sharing to advance medical discoveries and benefit people impacted by cancer in the future, (ii) transparency around researchers' credibility and their intentions for data sharing, (iii) level of ownership and control over data sharing, and (iv) protocols for privacy and confidentiality in data sharing. Based on these themes, we present practical strategies for optimizing data access and sharing in cancer research.

Resilience eDBT: Development and Usability Evaluation of a Dialectical Behavior Therapy Skills Training App for Eating Disorders.

Dialectical behavior therapy (DBT) is an evidence-based treatment for disorders characterized by recurrent binge eating. Yet, access to specialized treatment like DBT remains limited. To increase the accessibility of DBT, we developed a DBT skills training app (Resilience: eDBT) for the management of eating disorder (ED) symptoms. This paper delineates the developmental process of Resilience and tests its usability. Descriptive information on the development and features of Resilience is provided, including its framework, content structure and delivery formats, functionality, data storage procedure, and privacy protocols. Usability was assessed via a mixed methods approach in 10 symptomatic individuals. Qualitative data were organized based on an existing framework, which included six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance. Resilience demonstrated good usability via a Systems Usability Scale score of 85.5, which exceeded the recommended cutoff of 68. Positive aspects of the app, according to interview data, were the ease of use and the visual design, while the addition of peer support was suggested as an opportunity for improvement. A novel DBT-based app may serve as an acceptable, low-intensity option or adjunct to traditional treatment for targeting ED symptoms that emerge in daily life. However, notable limitations include the small sample size and the single time point at which the usability assessment was conducted.

PrivySeC: A secure and privacy-compliant distributed framework for personal data sharing in IoT ecosystems

The contemporary era experiences an unprecedented dependence on data generated by individuals via an array of interconnected devices constituting the Internet of Things (IoT). The information amassed through IoT devices serves many objectives, including prescriptive analytics and predictive maintenance, preemptive healthcare measures, disaster mitigation, operational efficiency, and increased yield. In contrast, most applications or systems that rely on user-generated data to fulfill their business objectives face challenges in adhering to privacy protocols. Consequently, users are exposed to many privacy risks. Such infringements upon privacy provisions give rise to apprehensions regarding the authenticity of the processed data. Hence, this paper presents weaknesses and challenges in current practices and proposes “PrivySeC,” a distributed ledger technology (DLT) based framework for privacy-preserving and secure sharing of personally and non-personally identifiable information. The security analysis indicates that the proposed solution ensures data privacy by design and complies with most of the requirements mandated by various privacy regulations. Similarly, PrivySeC promises low transaction latency and provides high throughput. Although we have created a privacy-preserving solution for sharing smart farm data, it can be customized to meet the specific privacy requirements of individual applications.

LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy Protocols

LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy Protocols

Introduction to Large Language Models (LLMs) for dementia care and research.

Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research. To this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs. We report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency. Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.

Allocation of Resources Efficiently in Tactile Ethernet PON’s - Healthcare

Efficient resource allocation plays a critical role in enhancing network performance within healthcare environments. This study focuses on resource allocation within Tactile Ethernet Passive Optical Networks (PONs) specifically designed for healthcare applications. Our investigation delves into the unique challenges and requirements posed by healthcare Local Area Networks (LANs), which encompass the imperative for secure and dependable data transmission, as well as the seamless integration of medical devices and tactile communication capabilities. Drawing upon a thorough review of existing literature and empirical analysis, this paper proposes novel strategies for the efficient distribution of resources within Tactile Ethernet PONs to address the diverse needs of healthcare networks. We explore innovative approaches to integrate tactile feedback seamlessly into network infrastructure, aiming to enhance user experience and streamline interaction with medical devices. Moreover, we examine the broader implications of efficient resource allocation in terms of its potential to elevate patient care standards, improve operational efficiency, and fortify data security and privacy protocols within healthcare settings. The insights gleaned from this research contribute to the ongoing endeavors aimed at optimizing network performance and enhancing usability across healthcare LANs, ultimately striving to elevate the quality of healthcare services delivered. Keywords- Tactile Ethernet, Passive Optical Network, Network Optimization.

Edge based content delivery scheme to ensure quality video streaming for online course material access in smart learning

In today’s Higher Education System (HES), Smart Learning (SL), also known as Intelligent Learning (IL) or Adaptive Learning (AL), plays an increasingly vital role. No longer is the traditional, one-size-fits-all method of education suitable for filling the several demands of students. Using SL technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms can potentially revolutionize the HES. An emerging area of study, edge-based SL helps use Edge Computing (EC) to provide learners with instantaneous, specialized, and context-aware learning. Internet of Things (IoT) devices are becoming increasingly well-liked, and data is proliferating. Using video data as a primary source of learning content and delivering it via EC infrastructure is what is referred to as “Video Streaming (VS)” in Edge-Based Learning (EBL). By examining the importance of providing mobile video clients with a high-quality visual experience—especially considering that video streaming (VS) traffic makes up a significant amount of mobile network traffic—the research gap is filled. The proposed Content Delivery Scheme (CDS), which is based on long short-term memory, is intended to improve security and privacy protocols, accelerate network service response times, and increase application intelligence. The project intends to close the current gap in edge-based Smart Learning (SL) technologies, namely in the distribution of video material for adaptive learning in higher education, by concentrating on these elements. Given that VS traffic forms a considerable portion of mobile network traffic, this paper aims to investigate the significance of delivering a performing visual experience to mobile video clients. Fast network service response, enhanced application intelligence, and enhanced security and privacy are all made possible by the proposed LSTM-based Content Delivery Scheme (CDS). The proposed approach attains minimal stall time of 2347 ms, which outperforms the existing techniques.

Popular logistics for collective unpredictabilities in pandemic Madrid

This article develops the concept of collective unpredictability as a socio-geographic gathering and sensibility for redescribing territories of urban habitation. It describes in detail the databases, privacy protocols, call centres, inventory and queue flow management systems, spatial surveys, food supply chains, transport operations, credit and voucher economies, and financial accounting systems designed by community organizations during the COVID-19 pandemic across six neighbourhoods in Madrid. It demonstrates how these various systems of attention, logistical geographies and ecologies of habitation threw into question the ceteris paribus assumptions underpinning the geographical epistemologies of biopolitical models. The emphasis on collective unpredictabilities aims not to confront expert factuals against community counterfactuals, but to insist on the urban liveliness of otherwisefactuals.

Differential Fault Attacks on Privacy Protocols Friendly Symmetric-Key Primitives: RAIN and HERA

As the practical applications of fully homomorphic encryption (FHE), secure multi-party computation (MPC) and zero-knowledge (ZK) proof continue to increase, so does the need to design and analyze new symmetric-key primitives that can adapt to these privacy-preserving protocols. These designs typically have low multiplicative complexity and depth with the parameter domain adapted to their application protocols, aiming to minimize the cost associated with the number of nonlinear operations or the multiplicative depth of their representation as circuits. In this paper, we propose two differential fault attacks against a one-way function RAIN used for Rainier (CCS 2022), a signature scheme based on the MPC-in-the-head approach and an FHE-friendly cipher HERA used for the RtF framework (Eurocrypt 2022), respectively. We show that our attacks can recover the keys for both ciphers by only injecting a fault into the internal state and requiring only one normal and one faulty ciphertext blocks. Thus, we can use only the practical complexity of 226.6/228.8/230.4 bit operations to break the full-round RAIN with 128/192/256-bit keys. For full-round HERA with 80/128-bit key, our attack is practical with complexity the complexity of 220 encryptions with about 216 memory.

A source location privacy protocol‐based energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in social Internet of Things

SummarySource location privacy is a developing research topic in the social Internet of Things. Source location privacy holds paramount importance in security critical wireless sensor network applications like tracking and monitoring. Several methods have been proposed for source location privacy in the social Internet of Things, but the existing methods have some issues such as improper path selection, the transmission of duplicate messages, and low network lifetime. To overcome these issues, a source location privacy protocol based on energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in the social Internet of Things is proposed in this manuscript. At first, the optimal route for the source is selected with the help of energy‐efficient and link‐reliable routing, this method helps to avoid improper path selection. To estimate the quality of the selected optimal path, the multi‐scale bifurcated deep Capsnet is applied. The introduced method is executed in MATLAB. The introduced method's performance is estimated with the aid of several performances evaluating metrics like sensitivity, energy consumption, network lifetime, safety period, and delay.

A Data Management System for Smart Cities Leveraging Artificial Intelligence Modeling Techniques to Enhance Privacy and Security

Smart cities are metropolitan areas that use sophisticated technology to increase efficiency, sustainability, and overall quality of life. The potential for transformation is tremendous, with applications ranging from Internet of Things (IoT)-driven infrastructure to data-driven governance. Effectively handling the abundant data produced in smart cities requires stringent security and privacy protocols. This research aims to tackle these difficulties by introducing the suggested Artificial Intelligence-based Data Management System (AI-DMS) for Smart Cities. AI-DMS seeks to optimize the data processing pipeline, guaranteeing effectiveness throughout the process, from data extraction to publication. Implementing a Multi-Level Sensitive Model is a notable addition, as it classifies data into three categories: sensitive, quasi-sensitive, and public. This allows for more nuanced sharing of data. Privacy preservation is accomplished using Principal Component Analysis (PCA), a comprehensive technique encompassing feature mapping, selection, normalization, and transformation. The simulation results demonstrate that AI-DMS outperforms other methods. It achieves a Data Quality Score of 95.12% (training) and 93.76% (testing), a Privacy Preservation Rate of 85.23% (training) and 82.76% (testing), a Processing Efficiency of 90.54% (training) and 88.76% (testing), a Sensitivity Model Accuracy of 80.12% (training) and 78.45% (testing), and a Data Access Time of 22.76 ms (training) and 21.32 ms (testing). The results highlight AI-DMS as a reliable and effective system, guaranteeing superior smart city data management that is secure and precise. This contribution aligns with the changing urban scene, offering improvements in decision-making based on data while still ensuring privacy and security.

RAMA: a risk assessment solution for healthcare organizations

Recent cyber-attacks targeting healthcare organizations underscore the growing prevalence of the sector as a prime target for malicious activities. As healthcare systems manage and store sensitive personal health information, the imperative for robust cyber security and privacy protocols becomes increasingly evident. Consequently, healthcare institutions are compelled to actively address the intricate cyber security risks inherent in their digital ecosystems. In response, we present RAMA, a risk assessment solution designed to evaluate the security status of cyber systems within critical domain, such as the healthcare one. By leveraging RAMA, both local stakeholders, such as the hospital’s IT personnel, and global actors, including external parties, can assess their organization’s cyber risk profile. Notably, RAMA goes beyond risk quantification; it facilitates a comparative analysis by enabling organizations to measure their performance against average aggregated mean scores, fostering a culture of continuous improvement in cyber security practices. The practical efficacy of RAMA is demonstrated through its deployment across four real-world healthcare IT infrastructures. This study not only underscores the significance of addressing cyber security risks within healthcare but also highlights the value of innovative solutions like RAMA in safeguarding sensitive health information and enhancing the sector’s overall cyber resilience.

A Cryptographic Protocol for Efficient Mutual Location Privacy Through Outsourcing in Indoor Wi-Fi Localization

A Cryptographic Protocol for Efficient Mutual Location Privacy Through Outsourcing in Indoor Wi-Fi Localization

Health Communication Film Implementation Strategy COVID-19-era: The Turning T.I.D.E. in HIV Multimedia Research

Among public health and social behavioral scientists there is an emergent interest in using film-based methodologies to promote health and wellness. During the novel coronavirus disease 2019 (COVID-19), this research method, as well as other forms of research, were thwarted. This article reports on our experience of implementing an online health communication film intervention under a research design initially intended for in-person communal viewing. We investigated if the film-based intervention could enhance the uptake of therapeutic modalities first learned through a brief five week session for Black and Latine adults living with HIV. To address changes in research methods attributed to COVID-19, the T.I.D.E. implementation method includes four critical considerations: 1) T- Tenacious approach, increased level of persistence leveraging social and clinical supports, 2) I- Immediate Needs, connecting participants to needed community resources, 3) D- Dissemination Plan revamp and harness technology to share information, and 4) E- Environmental Awareness privacy protocol to strengthen trust by participants.

Evaluating the validity and reliability of the Tswana adaptation of the MOS-HIV tool for health-related quality of life among HIV Sub-populations in Botswana: A study protocol.

The lack of culturally and contextually appropriate adaptations of health-related quality of life (HRQoL) tools hinders HIV patient outcomes. This study aims to assess the validity and reliability of a Tswana version of the Medical Outcome Survey-HIV (MOS-HIV) tool among diverse HIV sub-populations in Botswana. In terms of the methodology the study will comprise of several steps. Firstly, forward and back-translation of the original U.S. English MOS-HIV tool into Setswana, followed by the review of the translated tool. Phase 1 will evaluate content, construct validity, and reliability of the newly developed tool among HIV outpatients at Gaborone Infectious Diseases Clinics (IDCC). A Cronbach's alpha coefficient >0.7 across the 35 items and 11 dimensions of the MOS-HIV tool will indicate internal consistency reliability. Phase 2 will employ the use of logistic regression models to identify predictors of poor HRQoL among randomly selected IDCC sites, both in urban and rural centers. Phase 3 will investigate predictors of poor HRQoL among inpatients receiving treatment for HIV-associated cryptococcal meningitis (CM) using longitudinal data analysis methods. Ethical approval has been obtained from the University of Botswana, Walter Sisulu University, Human Research and Development Unit, Ministry of Health, and Princess Marina Hospital. Prospective participants will provide written informed consent, with proxy consent explored when feasible. Voluntary participation and confidentiality will be ensured during data collection and analysis. Data will be securely stored under lock-and-key. Dissemination of study findings will adhere to strict privacy protocols, avoiding the sharing of personal identifiers.

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

The integration of Information Systems (IS), Big Data Analytics (BDA), and Artificial Intelligence (AI) has ushered in a new era of energy-efficient design for rural residences. This study delves into the intricate synergy between technology and sustainability, unveiling the transformative potential of these tools in reshaping rural living spaces. The exploration spans from the conceptualization of designs to their real-world implementation, highlighting the pivotal role of IS in facilitating collaborative efforts among stakeholders. The study further uncovers the power of Big Data Analytics in deciphering energy consumption patterns, climatic variations, and occupant behaviours. These insights lay the groundwork for AI-powered simulations that optimize energy efficiency while ensuring occupant comfort. The study underscores the consequences of ineffective design, elucidating how it amplifies energy consumption, escalates environmental impact, and compromises residents' quality of life. In contrast, the integration of IS, BDA, and AI results in energy-efficient residences, marked by reduced energy usage, enhanced indoor comfort, and economic savings. Despite challenges such as limited resources, harsh climates, and technical expertise gaps, innovative solutions in the form of training programs, data privacy protocols, and collaborations emerge as beacons of progress. Looking to the future, emerging trends like smart grids, Internet of Things (IoT) integration, and AI-driven predictive maintenance shape the narrative of rural residences design. Rural communities stand poised for self-sufficiency and sustainability, empowered by the fusion of technology and ecological mindfulness. The recommendations presented in this study offer actionable insights for construction professionals, policymakers, and researchers, emphasizing interdisciplinary collaboration, continuous monitoring, and ongoing training. Future directions include greater investigation of new trends in sustainability, smart grids, and predictive maintenance, which will help rural communities become self-sufficient and environmentally conscientious.

IoT Security in the Era of Ubiquitous Computing: A Multidisciplinary Approach to Addressing Vulnerabilities and Promoting Resilience

The Internet of Things (IoT) has rapidly become a pivotal, transformative force, seamlessly integrating billions of physical devices through sophisticated networks of embedded sensors, software, and internet connectivity. This expansive and interconnected ecosystem offers a broad spectrum of applications, significantly benefiting urban infrastructure with innovative solutions, enhancing industrial operations through optimization, and enriching consumer experiences with smart devices for safety and convenience. Despite the numerous benefits, the widespread adoption of IoT technologies has challenges, particularly in security and privacy. The proliferation of IoT devices has opened up new avenues for potential cyber threats, posing risks of data breaches and privacy violations. An in-depth analysis of notable IoT security incidents, such as the 2015 Jeep Hack, the Owlet WiFi Baby Heart Monitor Hack, and the TRENDnet Webcam Hack, highlights the critical vulnerabilities inherent in many IoT systems.
 Organizations must adopt comprehensive and robust security measures to address these security concerns, including implementing advanced encryption protocols, deploying effective firewalls stringent access control mechanisms, and conducting regular security audits. A multi-layered security architecture becomes essential in mitigating such threats and ensuring the integrity of IoT networks. Furthermore, integrating blockchain technology presents a promising enhancement to IoT security and privacy protocols. Blockchain's inherent features of decentralization, transparency, and immutability offer an additional layer of security, making it more difficult for unauthorized entities to compromise IoT systems.
 Equally crucial is the need to elevate IoT security awareness among organizations; this can be achieved through persistent research, fostering collaborations with security experts, and promoting best practices in IoT security. By actively addressing these security challenges, organizations can not only harness the full potential of IoT but also protect their reputations, build trust with stakeholders, and ensure the privacy and safety of their data. Therefore, while IoT presents an array of opportunities for innovation and efficiency, the importance of vigilance in security cannot be overstated. Balancing the benefits of IoT with robust security measures will be vital to realizing its full potential safely and reliably.