Privacy Technology Research Articles

Can encryption save lives? Secure messaging and its infrastructure as loci of convergence between cyber warfare and conventional warfare: The case of Ukraine

The controversies surrounding the right to privacy of individuals in a hyperconnected world are longstanding debates, where particular emphasis is placed on encryption technologies, which encode information by converting its original representations into alternative forms that computers cannot decipher, thus ensuring the security of communications. These technologies are at the heart of a public controversy, in which privacy advocates a clash with claims that encryption is a threat to general security as an enabler of subversive action. Recent developments in the armed conflict in Ukraine open up or renew questions such as: in times of war, what is the role of encryption and privacy technologies? How does armed conflict challenge existing threat models, what are the new risks for civil society? Can encryption save lives? This article addresses these questions by showing that encrypted messaging is the subject of convergence between the informational and physical aspects ‘in the field’ of war in the 21st century. The aim is to show how these messaging tools and the digital ecosystem that makes their deployment possible (interfaces, access providers, telecom operators) are now an integral part of a war and resistance infrastructure where the borders between cyber warfare and conventional warfare are becoming more and more blurred. However, we will also underline the limits of a tool-centred approach and demonstrate how, in the case of the war in Ukraine, physical threats to civilians and infrastructure damage mean that encrypted messaging is one among several innovative technical and social practices of holistic self-defence deployed by Ukrainians.

GDPR Compliance of Hospital Management Systems in the UAE

While the UAE is making strides in healthcare digitalization and adopting global best practices, the absence of a unified data protection framework equivalent to the GDPR poses significant challenges for hospital management systems (HMS) in the region. This gap creates uncertainties in compliance, especially regarding cross-border data transfers, third-party vendor management, and the protection of patients' privacy rights. The lack of clear regulations tailored to the UAE’s unique healthcare landscape hinders the implementation of robust data protection measures, raising concerns about potential data breaches, legal liabilities, and the overall trustworthiness of healthcare institutions. Addressing these challenges is crucial for aligning the UAE’s healthcare sector with international standards while ensuring the security and privacy of patient data in a rapidly evolving digital environment. The General Data Protection Regulation (GDPR) has significantly impacted hospital management systems (HMS) by setting strict data protection requirements. This study provides a systematic literature review of GDPR compliance in HMS, focusing on key challenges such as regulatory complexity, permission management, data subject rights, data breaches, third-party vendor management, and cross-border data transfers. Suggested mitigation measures include privacy by design, data protection impact assessments, improved consent management, robust breach detection, and efficient vendor management. Legislative reforms are needed to clarify GDPR's application to healthcare. The study also highlights increased investments in privacy technologies, improved patient trust, and the demand for advanced solutions. Future research should explore the effectiveness of these mitigations, GDPR's impact on patient satisfaction, ethical data processing, and standardized data protection frameworks in healthcare. Achieving GDPR compliance is crucial for protecting patient data, building trust, and ensuring secure and ethical use of healthcare information. This study aims to guide healthcare organizations, particularly hospitals, along with regulators and researchers, in navigating these challenges and implementing effective solutions.

Overview of homomorphic encryption technology for data privacy

This study examines the overview of homomorphic encryption technology for data privacy. In the era of big data, the growing need to utilize vast amounts of information while ensuring privacy and security has become a significant challenge. Homomorphic encryption technology has gained attention as a solution for privacy-preserving data processing, allowing computations on encrypted data without exposing sensitive information. This study introduces the concept of data privacy preservation and explores the evaluation of homomorphic encrypted technology. The focus is on analyzing both partial and full homomorphic encryption methods, highlighting their respective characteristics, evaluation criteria, and the current state of research. Partial homomorphic encryption supports limited operations, while full homomorphic encryption enables unlimited computation on encrypted data, though both face challenges related to computational overhead and efficiency. Additionally, this paper addresses the ongoing issues and limitations associated with homomorphic encryption, such as its complexity, large encryption volumes, and difficulties in handling large-scale datasets. Despite these challenges, researchers continue to refine the technology and expand its applications in cloud computing, big data analytics, and privacy-preserving computing environments. This study also discussed potential future research avenues aimed at improving the scalability, efficiency, and security of homomorphic encryption to support broader, real-world applications. Ultimately, homomorphic encryption is positioned as a key enabler for secure data utilization in an increasingly privacy-conscious digital landscape.

WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics data

Open Data Processing Services are used to solve the bottleneck of big data storage and operation. At the same time, massive trajectory data is generated, and the basic information of users’ spatio-temporal historical data is provided, including points of interest and movement patterns. Improving the availability of published trajectory statistics data without compromising user privacy is critical. Differential privacy technology is a standard technology to realize the secure release of trajectory statistics data. Several research efforts have focused on secure publication of trajectory statistics data in a central environment by adding noise to a trusted third-party server. However, this central approach is vulnerable to privacy breaches, where adversaries can access real data by locking down the third-party server. The local differential privacy, based on a distributed architecture, overcomes this form of attack by allowing users to scramble personal data records before they are sent to third-party server. However, the existing distributed privacy protection schemes still have the balance problem of poor availability of data when ensuring privacy, as well as the problem of excessive operation cost. Therefore, a local differential privacy mechanism based on water-filling for secure release of trajectory statistics data (WF-LDPSR) is proposed in this paper. Firstly, in order to protect user privacy individually, a user automatic personalized segmentation method is proposed to determine the effective user sensitivity level automatically. Secondly, a distributed privacy protection model based on local differential privacy is designed to resist the attacks on the third-party server. Finally, in order to achieve the optimal allocation of privacy budget, the water-filling theorem in the field of communication is introduced. An adaptive privacy budget allocation algorithm based on water-filling theorem is proposed to realize the adaptive privacy budget allocation. In addition, to further improve data availability, a group processing idea based on user set sampling is proposed, which divides users into multiple disjoint subsets randomly, thus reducing the differential privacy noise effectively. Experiments prove that compared with other advanced mechanisms, the WF-LDPSR mechanism can improve the availability of published data by 84.92% while protecting user privacy.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

A graph representation framework for encrypted network traffic classification

Network Traffic Classification (NTC) is crucial for ensuring internet security, but encryption presents significant challenges to this task. While Machine Learning (ML) and Deep Learning (DL) methods have shown promise, issues such as limited representativeness leading to sub-optimal generalizations and performance remain prevalent. These problems become more pronounced with advanced obfuscation, network security, and privacy technologies, indicating a need for improved model robustness. To address these issues, we focus on feature extraction and representation in NTC by leveraging the expressive power of graphs to represent network traffic at various granularity levels. By modeling network traffic as interconnected graphs, we can analyze both flow-level and packet-level data. Our graph representation method for encrypted NTC effectively preserves crucial information despite encryption and obfuscation. We enhance the robustness of our approach by using cosine similarity to exploit correlations between encrypted network flows and packets, defining relationships between abstract entities. This graph structure enables the creation of structural embeddings that accurately define network traffic across different encryption levels. Our end-to-end process demonstrates significant improvements where traditional NTC methods struggle, such as in Tor classification, which employs anonymization to further obfuscate traffic. Our packet-level classification approach consistently outperforms existing methods, achieving accuracies exceeding 96%.

The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions

This article explores the critical role of deep learning in developing AI-driven cybersecurity solutions, with a particular focus on privacy integrity and information security. It investigates how deep neural networks (DNNs) and advanced machine learning techniques are being used to detect and neutralize cyber threats in real time. The article also considers the implications of these technologies for data privacy, discussing the potential risks and benefits of using AI to protect sensitive information. By examining case studies and current research, the piece provides insights into how organizations can deploy deep learning models to enhance both security and privacy integrity in a digital world.

Optimizing task allocation with temporal‐spatial privacy protection in mobile crowdsensing

AbstractMobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.

Emotional AI in education and toys: Investigating moral risk awareness in the acceptance of AI technologies from a cross-sectional survey of the Japanese population

Emotional artificial intelligence (AI), i.e., affective computing technologies, is rapidly reshaping the education of young minds worldwide. In Japan, government and commercial stakeholders are promulgating emotional AI not only as a neoliberal, cost-saving benefit but also as a heuristic that can improve the learning experience at home and in the classroom. Nevertheless, critics warn of a myriad of risks and harms posed by the technology such as privacy violation, unresolved deeper cultural and systemic issues, machinic parentalism as well as the danger of imposing attitudinal conformity. This study brings together the Technological Acceptance Model and Moral Foundation Theory to examine the cultural construal of risks and rewards regarding the application of emotional AI technologies. It explores Japanese citizens’ perceptions of emotional AI in education and children's toys via analysis of a final sample of 2000 Japanese respondents with five age groups (20s–60s) and two sexes equally represented. The linear regression models for determinants of attitude toward emotional AI in education and in toys account for 44 % and 38 % variation in the data, respectively. The analyses reveal a significant negative correlation between attitudes toward emotional AI in both schools and toys and concerns about privacy violations or the dystopian nature of constantly monitoring of children and students’ emotions with AI (Education: βDystopianConcern = − .094***; Toys: βPrivacyConcern = − .199***). However, worries about autonomy and bias show mixed results, which hints at certain cultural nuances of values in a Japanese context and how new the technologies are. Concurring with the empirical literature on the Moral Foundation Theory, the chi-square (Χ2) test shows Japanese female respondents express more fear regarding the potential harms of emotional AI technologies for the youth's privacy, autonomy, data misuse, and fairness (p < 0.001). The policy implications of these results and insights on the impacts of emotional AI for the future of human-machine interaction are also provided.

Protecting Privacy of Pregnant and LGBTQ+ Research Participants

This Viewpoint summarizes existing federal regulations aimed at protecting research data, describes the challenges of enforcing these regulations, and discusses how evolving privacy technologies could be used to reduce health disparities and advance health equity among pregnant and LGBTQ+ research participants.

Develop and Disseminate Hands-on Lab Materials of Privacy Concepts and Technologies to Educators

In the era of digitalization, a massive amount of data has been generated from people’s online activities or use of portable/wearable devices. The data often carries rich information about people. Therefore, privacy technologies are needed, from data generation to usage and from transmission to storage, to protect people’s sensitive information. Although the research community is making great progress in addressing advanced privacy protection technologies, very few educational materials have been developed to incorporate the latest research results and engage students in learning privacy technologies, especially for younger generations. In this paper, we present our newly designed educational materials on privacy technologies, which can be used for training high quality cybersecurity professionals to meet the ever-increasing demand. The developed learning modules not only incorporate the latest research results in privacy technologies but also include effective hand-on lab activities. To help other institutions effectively teach privacy technologies, we organized a faculty training workshop in summer 2022. Twenty-nine faculty from twenty institutions nationwide participated in the training. Survey results show that the participants gained a better understanding of privacy issues and demonstrated strong interest in teaching privacy technologies after attending the workshop.

Research on machine learning technology with privacy protection strategy in recommendation field

Technologies such as machine learning can achieve accurate personalized recommendations. However, due to the collection and utilization of a large amount of user information in this process, people are widely worried about data security and privacy issues. This paper first introduces two key issues of privacy protection in the field of machine learning, namely data privacy and model privacy. On this basis, this paper introduces and analyzes homomorphic encryption, differential privacy and federated learning, and compares their advantages and disadvantages. Among them, homomorphic encryption technology has a large computational cost, differential privacy technology has a negative impact on system accuracy, and federated learning technology has a high training and communication cost. Therefore, it will be the future research direction to study more efficient and accurate recommendation models.

Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme

To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.

Application of privacy protection technology to healthcare big data.

With the advent of the big data era, data security issues are becoming more common. Healthcare organizations have more data to use for analysis, but they lose money every year due to their inability to prevent data leakage. To overcome these challenges, research on the use of data protection technologies in healthcare is actively underway, particularly research on state-of-the-art technologies, such as federated learning announced by Google and blockchain technology, which has recently attracted attention. To learn about these research efforts, we explored the research, methods, and limitations of the most widely used privacy technologies. After investigating related papers published between 2017 and 2023 and identifying the latest technology trends, we selected related papers and reviewed related technologies. In the process, four technologies were the focus of this study: blockchain, federated learning, isomorphic encryption, and differential privacy. Overall, our analysis provides researchers with insight into privacy technology research by suggesting the limitations of current privacy technologies and suggesting future research directions.

Optimization of Privacy Budget Allocation in Differential Privacy Technology: An Empirical Study

Optimization of Privacy Budget Allocation in Differential Privacy Technology: An Empirical Study

Privacy-Conflict Resolution for Integrating Personal and Electronic Health Records in Blockchain-Based Systems.

This paper suggests using blockchain technology for privacy concerns in integrating personal health records and electronic health records in decentralized e-healthcare systems. This report focuses on designing automatic conflict resolution techniques to ensure patient data ownership, transparency, and privacy in inter-organizational processes. This paper proposes designing automatic conflict resolution techniques in decentralized e-healthcare systems, which can improve inter-organizational processes in healthcare. Using blockchain technology to integrate personal and electronic health records can ensure patient data ownership and promote transparency in data usage, addressing privacy concerns in healthcare systems. This paper emphasizes the importance of data privacy and protection in healthcare systems, highlighting the need for compliance with laws and regulations. The research results, including the proof-of-concept prototype, can provide practical insights into implementing conflict resolution techniques in decentralized e-healthcare systems.

Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) with blockchain-based incentive mechanism

The Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) represents a progressive solution for resolving trust-related issues linked to third-party auditors (TPAs) within traditional healthcare cloud storage audit systems. As the healthcare industry increasingly relies on cloud storage, concerns surrounding security and privacy have grown, motivating the development of a unique incentive mechanism. This mechanism leverages the non-tamperable and traceable features of blockchain technology to encourage TPAs to uphold honesty and reliability. By organizing TPAs as a group of nodes on the blockchain, a system of mutual surveillance is established, enabling diligent monitoring and penalization of any malicious actions. TCSHAS encompasses a comprehensive system model that incorporates smart contracts to manage transaction-related matters, including dispute resolution. Performance evaluations have confirmed its efficiency and suitability for real-world healthcare applications. Our experiments demonstrate that TCSHAS performs well in terms of gas consumption and exhibits scalability as the number of participant’s increases. Compared with other common smart contracts, TCSHAS maintains a balanced level of complexity, incorporates strong security measures, offers comprehensive auditing capabilities, and remains flexible. To further enhance our research, we can explore ways to optimize TCSHAS scalability and performance, such as through shading, as the volume of healthcare data continues to grow. In addition, we can investigate the integration of advanced privacy technologies or AI-based auditing. These advancements will reinforce the role of TCSHAS in establishing trust and security in healthcare cloud storage audits, making it highly relevant in real-world healthcare settings. Ultimately, TCSHAS contributes to improving trust and security in healthcare cloud storage auditing, ensuring responsible management of sensitive healthcare data.

Future-self vividness influences acceptance of camera-based active and assisted living technologies

Abstract Background Camera-based active and assisted living (AAL) technologies are an eminent solution to the accruing burdens of population ageing but are hardly accepted by older adults. The technology's privacy implications and perceived usefulness are established antecedents to its acceptance. However, one understudied barrier to acceptance is a failure in future self-identification. Specifically, to the extent that the wellbeing benefits of camera-based AAL technologies largely accrue to the future (rather than the present) self, older adults who have less (versus more) vivid impressions of their future selves may be less willing to accept the technology today. Objectives To assess the unique contribution of future self-vividness to the prediction of older adults’ acceptance of camera-based AAL technologies. Methods A cross-sectional online survey collected data from n = 183 community-dwelling older adults (mean age = 64.2, 51.9% male). Hierarchical logistic regression was used to evaluate the association of future self-vividness and acceptance, independent of demographics, privacy concerns, and the perceived usefulness of camera-based AAL technologies. Results Future self-vividness was positively associated with an increased likelihood of acceptance (OR = 2.54, 95% CI 1.26-5.11) even after controlling for the effects of demographics, perceived usefulness, and privacy concerns. Compared to a demographics-only model (Nagelkerke R2 = .256) and subsequent model that included privacy concerns and perceived usefulness as additional predictors (Nalgekerke R2 = .439), the addition of future self-vividness added significantly to the prediction of acceptance (χ2 = 7.58, p = .006, Nalgekerke R2 = .495). Conclusions Acceptance of camera-based AAL technologies is uniquely influenced by future self-vividness. Enhancing the vividness with which older adults envisage their future selves may be an important strategy to promote acceptance and use of camera-based AAL technologies. Key messages • Future self-vividness is uniquely associated with the acceptance of camera-based AAL technologies. • Interventions that enhance future self-vividness may promote acceptance of AAL technologies.

Anonymity Maintenance Algorithm (Ama) Of Health Care Data from IoT Devices Using Edge System with Transfer Learning

In order to fully leverage the potential of the Internet of Things (IoT), it is crucial to safeguard against potential threats and prioritize the security and privacy of data collected and extracted from IoT devices. However, the implementation of security and privacy technology within IoT presents a number of unique challenges. This is due to the fact that IoT solutions comprise a diverse array of security and privacy solutions that are designed to safeguard IoT data at the device, infrastructure/platform, and application layers. As a result, ensuring end-to-end privacy protection across these three layers is a significant challenge within IoT. IoT devices implemented with recent advancements utilize edge system and monitors fitness of a person privately. In our proposed concept anonymity maintenance done along with privacy preserving in query processing in accordance with health monitoring values. In this edge system transfer learning applied to transfer minimum data which is privacy preserved to equip edge system about IoT devices in spite of their location. De-duplication will be done in edge system in health values monitoring thus frequency learning will be inculcated to know the frequency of that device in specified location. In health-oriented processing of query the privileges will be granted for each category of people to access integrity of data according to their standard of authentication. While granting privileges concerned person can handle all their available personal data using their authentic login details. Best edge system will be chosen using PSO or GWO techniques thus de-duplication implemented efficiently.

Data Privacy, Conceptual Framework for IoT Based Devices in Healthcare: A Systematic Review

With the rollout of the Fifth-Generation network, more Internet of Things (IoT) devices tend to increase, which increases the amount of data being shared by the devices. It is complex to secure data transmission and device-to-device communication due to the vast number of IoT devices and the complexity of networks. The Internet of Things (IoT) has evolved to enable everyday things and gadgets to connect digitally and communicate with one another, using technologies that send, gather, and analyse data from people using those objects and devices. IoT data privacy risks are widespread use. The primary objective of information technology (IT) security in Web 2.0 was to ensure the privacy, accuracy, and reliability of systems and communications. As a result of IoT devices’ often limited CPU power for longer-lasting activities, these conventional metrics, however, exhibit measurable constraints. IoT security is, therefore, critical in the context of guaranteeing security through the data privacy of IoT users. The author conducted a literature methodological analysis on the data privacy framework that will help to safeguard the 5G IoT-enabled devices on user data, technologies for data privacy in 5 G-based IoT devices, data privacy dangers associated with 5G IoT devices, and data privacy attack surfaces in 5G IoT devices