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Mobile government (m-government) has been proposed as a means to rebuild public trust in government. However, there is limited discussion regarding how m-government shapes trust in the existing literature. Thus, our study aims to examine the impact of m-government services on various types of trust by using the concept of relative trust. Additionally, we investigate how public value creation, private value acquisition, and risk perception mediate the relationship between m-government use and citizens' relative trust in government. To test our hypotheses, this study collected data from a telephone survey of 2,875 public service users in China. The findings reveal that the use of m-government services is positively associated with citizens' relative trust in institutions but negatively associated with their relative trust in grassroots civil servants. Furthermore, perceived public and private values mediate the relationship between m-government services and citizens' relative trust in institutions versus grassroots civil servants, with private value playing a more significant mediating role. This study provides evidence on the role of m-government services in shaping various types of citizens' relative trust in government, including institutional trust and relational trust. [ABSTRACT FROM AUTHOR]

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets. [ABSTRACT FROM AUTHOR]

Purpose – Data breaches in the US healthcare sector have more than tripled in the last decade across all states. However, to this day, no established framework ranks all states from most to least at risk for healthcare data breaches. This gap has led to a lack of proper risk identification and understanding of cyber environments at state levels. Design/methodology/approach – Based on the security action cycle, the National Institute of Standards and Technology (NIST) cybersecurity framework, the risk-planning model, and the multicriteria decision-making (MCDM) literature, the paper offers an integrated multicriteria framework for prioritization in cybersecurity to address this lack and other prioritization issues in risk management in the field. The study used historical breach data between 2015 and 2021. Findings – The findings showed that California, Texas, New York, Florida, Indiana, Pennsylvania, Massachusetts, Minnesota, Ohio, and Georgia are the states most at risk for healthcare data breaches. Practical implications – The findings highlight each US state faces a different level of healthcare risk. The findings are informative for patients, crucial for privacy officers in understanding the nuances of their risk environment, and important for policy-makers who must grasp the grave disconnect between existing issues and legislative practices. Furthermore, the study suggests an association between positioning state risk and such factors as population and wealth, both avenues for future research. Originality/value – Theoretically, the paper offers an integrated framework, whose basis in established security models in both academia and industry practice enables utilizing it in various prioritization scenarios in the field of cybersecurity. It further emphasizes the importance of risk identification and brings attention to different healthcare cybersecurity environments among the different US states.

Abstract Background Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically generated samples. In this article, we provide a discussion on existing methods and recommendations when evaluating the quality of synthetic longitudinal EHRs. Methods We recommend to assess synthetic EHR quality through similarity to real EHRs in low-dimensional projections, accuracy of a classifier discriminating synthetic from real samples, performance of synthetic versus real trained algorithms in clinical tasks, and privacy risk through risk of attribute inference. For each metric we discuss strengths and weaknesses, next to showing how it can be applied on a longitudinal dataset. Results To support the discussion on evaluation metrics, we apply discussed metrics on a dataset of synthetic EHRs generated from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) repository. Conclusions The discussion on evaluation metrics provide guidance for researchers on how to use and interpret different metrics when evaluating the quality of synthetic longitudinal EHRs.

Background: Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically generated samples. In this article, we provide a discussion on existing methods and recommendations when evaluating the quality of synthetic longitudinal EHRs. Methods: We recommend to assess synthetic EHR quality through similarity to real EHRs in low-dimensional projections, accuracy of a classifier discriminating synthetic from real samples, performance of synthetic versus real trained algorithms in clinical tasks, and privacy risk through risk of attribute inference. For each metric we discuss strengths and weaknesses, next to showing how it can be applied on a longitudinal dataset. Results: To support the discussion on evaluation metrics, we apply discussed metrics on a dataset of synthetic EHRs generated from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) repository. Conclusions: The discussion on evaluation metrics provide guidance for researchers on how to use and interpret different metrics when evaluating the quality of synthetic longitudinal EHRs. [ABSTRACT FROM AUTHOR]

Purpose: Most studies focus on the advantages of adopting innovations, such as Internet banking and mobile banking, assuming that new technology should be adopted because it is good enough. However, very few have specifically examined the risks perceived by customers when using DPM, especially mobile banking. Therefore, this study aims to determine the influence of privacy risk, security risk, access risk, personalization risk, and trust risk on the use of digital payments. Design/Methodology/Approach: This study is explanatory. The sampling technique used is purposive sampling where the sample is taken with certain considerations or conditions. The criteria are as follows: 1) the millennial generation aged 18 to 45 years and 2) the selected sample is an active user of DPM such as SMS, internet, and mobile banking. The distribution of questionnaires used purposive convenience sampling to select samples. Convenience sampling is based on the availability of elements and the ease of obtaining them. To test the hypothesis, the Partial Least Square (PLS) technique was used using the smart PLS 3.0 application. Findings: personalization risk and trust risk variables have a significant positive effect on the use of digital payments. At the same time the access risk, security risk, and privacy risk variables do not affect the use of digital payments. Implications/Originality/Value: This study can enrich the literature on Perceived Risk (PR) theory by providing insight into how the risk perceived by users affects the adoption and use of digital payment methods. This can help understand the psychological and emotional factors that influence consumer decisions and open up opportunities to integrate PR Theory with other theories, such as the Technology Acceptance Model (TAM) or the Theory of Planned Behavior (TPB), to get a more comprehensive picture of technology adoption.

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants. [ABSTRACT FROM AUTHOR]

With the rise of the Internet of Things (IoT), social networks, and mobile devices, vast amounts of mobility data are continuously generated. These data encompass diverse location information from various sources, including smart vehicles, sensors, wearables, and social media platforms. By leveraging these data, we explore the semantic enrichment of trajectory components related to moving objects and locations, bringing the so-called multiple-aspects trajectories and relative privacy issues. Privacy risk analysis is crucial for the earlier detection of privacy problems, particularly when dealing with semantically enriched trajectories. In this study, we introduced the TrajectGuard privacy risk assessment framework. TrajectGuard, an extension of PRUDEnce, achieved significant results by formulating and assessing the privacy risk of multiple-aspects trajectories under several proposed attacks. The framework introduced a nuanced risk evaluation using AspectGuard and conducted fair privacy assessments on anonymized datasets using AnonimoGuard. Its adaptability and versatility make TrajectGuard a valuable tool for preserving data privacy with multiple-aspects.

This research undertakes a thorough investigation into the adoption of social robots, an emerging field of significance as these technologies become more ingrained in daily life. By focusing on critical aspects that shape social robot adoption, this study illuminates the key determinants that influence user intentions. Using partial least squares structural equation modeling (PLS-SEM), the model was analyzed through a significant sample of potential users. The study revealed that human-like qualities such as appearance and behavior play a vital role in perceived enjoyment and social attraction. Furthermore, these factors positively affected perceived value, significantly steering adoption intention. Interestingly, privacy risk increased overall privacy concerns, yet it had no substantial effect on perceived value. Additionally, prior experience with pets was found to negatively influence the relationship between the social robot’s appearance and social attraction, providing unique insights that have valuable implications for various industry stakeholders.

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.

The advent of intelligent technologies, notably Brain–Computer Interfaces (BCIs), has introduced novel privacy dilemmas. Ensuring judicious privacy transfer is imperative for the application of BCI technology and pivotal for fostering economic and technological progress. This study adopts privacy transfer as the research perspective and employs an ethical matrix as the research method. It establishes BCI users as the central core interests, with marketers, developers, and medical personnel as stakeholders. Departing from the binary opposition of public and private in traditional privacy theory, this article proposes ethical principles such as maximizing benefits, minimizing harm, and respecting independent decision-making power. It constructs a judgment matrix for the privacy transfer of BCIs, utilizing this matrix to identify ethical risks like privacy disclosure and hijacking. This study analyzes the reasons for risks, aiming to overcome dilemmas and construct an ethical matrix to explore privacy transfer boundary division methods suitable for BCI technology and tailored to different stakeholders. [ABSTRACT FROM AUTHOR]

As the number of Internet users grows, the increase in smart devices interconnected through the Internet of Things (IoT) have contributed to improvements in the functionality of everyday products and enhancement of user experience. Yet, they affect user privacy and render personal data more vulnerable. To foster a digital future fully aware of user privacy requirements, a line of design research emerges that focuses on balancing product innovation with user data protection. This matter relates to sociocultural, economic, and technological aspects, and its core is a human-centered design strategy. Still, there is a gap in academic research oriented towards guiding product developers on how to consider personal data privacy concerns when designing honest IoT devices. To define this gap and delve deeper into this relevant topic, this paper presents a systematic literature review of recent academic research using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. This review focuses on prevalent research topics such as data privacy, personal data, data surveillance, and user behaviour in IoT. The result is a state-of-the-art compilation of 45 scientific studies mapping the most relevant concepts and approaches for product development in the last ten years of research, aligned with some central research questions. The Discussion and Conclusion sections provide a deep understanding of the complexity of the fast-changing landscape of privacy and personal data management using IoT products. Finally, this study proposes future academic research directions devoted to providing product designer specific, specialised help from different (yet interconnected) scientific approaches.

The study aimed to address the challenges in understanding the privacy goals of differential privacy by analyzing the privacy controversies surrounding it in various fields.It began with the example of data correlation and highlighted the differing perspectives among scholars regarding the targets of privacy protection.In cases where records in a dataset were correlated, adversaries can exploit this correlation to infer sensitive information about individuals, thereby sparking a debate on whether this violates privacy protection.To investigate the influence of privacy theories in the legal domain on defining privacy, two mainstream privacy theories in the computer field were examined.The first theory, limited access to personal information, focuses on preventing others from accessing an individual’s sensitive information.According to this theory, privacy mechanisms should aim to prevent adversaries from accessing a user’s actual information.In contrast, the second theory, control over personal information, emphasizes an individual’s right to communicate personal information to others.This theory suggests that the disclosure of personal information due to the relevance of others sharing data should not be considered a breach of privacy.Then the controversies of differential privacy were analyzed in the fields of computer science, social science, ethics and human-computer interaction due to their different understandings of privacy.By exploring the privacy concept of differential privacy from a multidisciplinary perspective, this study helps readers gain a correct understanding of the privacy viewpoint and goals of differential privacy while enhancing their understanding of the concept of “privacy” itself.

In the context of globalization, cross-border data circulation has brought many challenges to international investment law. Based on fuzzy theory, this paper constructs the affiliation function of cross-border data flow for the fuzzy set of international investment law based on fuzzy theory statistical experiment strategy, then establishes the fuzzy numerical analysis method, and uses the entropy weight method to strengthen the connection between the weights and the initial data for the problem that the index weights are not objective enough. The entropy-weight-based fuzzy numerical analysis method will then study the privacy risk, management risk, and strategic risk brought by cross-border data growth to international economic cooperation. From 2010 to 2019, the privacy risk of cross-border flowing data increased by 32.16% on average, the management risk increased by 37.84% on average, and the strategic risk increased by 167.31% on average. Although none of these 3 risk assessments have yet exceeded 50%, they still challenge major economies to balance the regulation of cross-border data security and promote capital cooperation and circulation. Faced with the challenges brought by cross-border data flow, countries should start by improving international investment agreements and enhancing international consensus, take advantage of the advantages of large data resource countries, and promote the establishment of new rules for cross-border data flow.

Romance has never felt the same way since the inception of online dating. A match is made not in Heaven but in the Cloud. Swiping profiles could sweep someone’s feet off. This work aims to assess the factors predicting the continuous use intention of online dating applications among young Malaysians through the lens of the UTAUT2 model and privacy risk. The research model was analyzed using Partial Least Squares Structural Equation Modelling per 282 valid responses from online daters in Malaysia. The results indicate the model’s substantial predictive power with social influence, price value, and habit as significant drivers to continuous use intention of online dating applications. Habit has the strongest effect on the intention to use online dating applications continuously. This research adds to the body of knowledge on the behavioral intention of online dating applications and offers practical suggestions for online dating service providers to increase the likelihood of their continuous adoption.

With the rapid development of artificial intelligence, privacy threats are already getting the spotlight. One of the most common privacy threats is the membership inference attack (MIA). Existing MIAs can effectively explore the potential privacy leakage risks of deep neural networks. However, DNNs are usually compressed for practical use, especially for edge computing, MIA will fail due to changes in DNNs' structure or parameters during the compression. To address this problem, we propose CM-MIA, an MIA against compression models, which can effectively determine their privacy leakage risks before deployment. In specific, firstly we use a variety of compression methods to help build shadow models for different target models. Then, we use these shadow models to construct sample features and identify abnormal samples by calculating the distance between each sample feature. Finally, based on the hypothesis test, we determine whether the abnormal sample is a member of the training dataset. Meanwhile, only abnormal samples are used for membership inference, which reduces time costs and improves attack efficiency. Extensive experiments are conducted on 6 datasets to evaluate CM-MIA's attack capacity. The results show that CM-MIA achieves the state-of-the-art attack performance in most cases. Compared with baselines, the attack success rate of CM-MIA is increased by 10.5% on average. [ABSTRACT FROM AUTHOR]

The development of peer-to-peer (P2P) accommodation market has seen multiple platforms providing booking services to consumers. As such consumers' switching intention is equally important as repurchase intention to platform operators. This study applies cue utilization theory to study the effects of perceived corporate social responsibility (CSR) on P2P accommodation consumers' repurchase intention and switching intention through trust and privacy risk. The study results show that the three dimensions (i.e. environmental, economic, and ethical) of perceived CSR have positive effects on trust, which increases repurchase intention but reduces switching intention; the environmental dimension of perceived CSR has a negative effect on privacy risk, which reduces repurchase intention but increases switching intention. Trust and privacy risk both mediate the effects of perceived CSR on repurchase/switching intention, but the indirect effects through trust are larger. The study demonstrates the utility of CSR in P2P accommodation platforms' marketing and customer relationship management strategies. 点对点 (P2P) 住宿市场的发展已经看到多个平台为消费者提供预订服务. 因此者对于平台经营者而言, 消费者的转换意愿与复购意愿同等重要. 本研究运用线索利用理论, 通过信任和隐私风险, 研究感知企业社会责任 (CSR) 对P2P住宿消费者的回购意愿和转换意愿的影响. 研究结果表明, 感知CSR的三个维度 (即环境、经济和伦理) 对信任有正向影响, 增加了回购意愿但降低了转换意愿; 感知CSR的环境维度对隐私风险有负面影响, 降低了回购意愿, 但增加了转换意愿. 信任和隐私风险都调解感知 CSR 对回购/转换意图的影响, 但通过信任产生的间接影响更大. 该研究证明了企业社会责任在 P2P 住宿平台的营销和客户关系管理策略中的效用. [ABSTRACT FROM AUTHOR]

User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors' rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors' ratings, which unfortunately reduces the accuracy of UserKNN. In this work, we introduce ReuseKNN, a novel differentially private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy and (ii) most users do not need to be protected with differential privacy since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations. Firstly, ReuseKNN requires significantly smaller neighborhoods and, thus, fewer neighbors need to be protected with differential privacy compared with traditional UserKNN. Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN. [ABSTRACT FROM AUTHOR]

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In traditional centralized Android malware classifiers based on machine learning, the training sample uploaded by users contains sensitive personal information, such as app usage and device security status, which will undermine personal privacy if used directly by the server. Federated-learning-based Android malware classifiers have attracted much attention due to their privacy-preserving and multi-party joint modeling. However, research shows that indirect privacy inferences from curious central servers threaten this framework. We propose a privacy risk evaluation framework, FedDroidMeter, based on normalized mutual information in response to user privacy requirements to measure the privacy risk in FL-based malware classifiers. It captures the essential cause of the disclosure of sensitive information in classifiers, independent of the attack model and capability. We performed numerical assessments using the Androzoo dataset, the baseline FL-based classifiers, the privacy-inferred attack model, and the baseline methodology of privacy evaluation. The experimental results show that FedDroidMeter can measure the privacy risks of the classifiers more effectively. Meanwhile, by comparing different models, FL, and privacy parameter settings, we proved that FedDroidMeter could compare the privacy risk between different use cases equally. Finally, we preliminarily study the law of privacy risk in classifiers. The experimental results emphasize the importance of providing a systematic privacy risk evaluation framework for FL-based malware classifiers and provide experience and a theoretical basis for studying targeted defense methods. [ABSTRACT FROM AUTHOR]

Inappropriate management of information security of e-commerce websites exerts a negative effect on consumers' purchase decisions because it may cause consumer data leakage. To mitigate this negative effect, some e-commerce firms such as Taobao in China voluntarily offer privacy breach insurance to consumers. Even though this fresh management of information security is becoming popular in practice, there are still rare academic studies concerning this hot topic. To fill this gap, this paper constructs a multi-stage game-theoretic model to examine the impacts of privacy breach insurance on a monopoly firm and to identify the market condition under which the firm would like to employ privacy breach insurance. We find that when consumers' data leakage concern is low and firm's data leakage possibility is low, privacy breach insurance always encourages more consumers to purchase. Otherwise, privacy breach insurance stimulates purchase only when the compensation of privacy breach insurance remains high. Furthermore, we reveal that the monopoly firm would provide privacy breach insurance when consumers' data leakage concern remains low. These fresh results suggest that privacy breach insurance acts as an efficient measure e-commerce firms could take to address increasingly serious information security threats. [ABSTRACT FROM AUTHOR]

Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual’s privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.

Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of 'user-environment-smart home product'. A total of 35 privacy risk scenarios of 'component-threat-failure-model-incident' combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems. [ABSTRACT FROM AUTHOR]

Online users are responsible for protecting their online privacy themselves: the mantra is custodiat te (protect yourself). Even so, there is a great deal of evidence pointing to the fact that online users generally do not act to preserve the privacy of their personal information, consequently disclosing more than they ought to and unwisely divulging sensitive information. Such self-disclosure has many negative consequences, including the invasion of privacy and identity theft. This often points to a need for more knowledge and awareness but does not explain why even knowledgeable users fail to preserve their privacy. One explanation for this phenomenon may be attributed to online privacy fatigue. Given the importance of online privacy and the lack of integrative online privacy fatigue research, this scoping review aims to provide researchers with an understanding of online privacy fatigue, its antecedents and outcomes, as well as a critical analysis of the methodological approaches used. A scoping review based on the PRISMA-ScR checklist was conducted. Only empirical studies focusing on online privacy were included, with nontechnological studies being excluded. All studies had to be written in English. A search strategy encompassing six electronic databases resulted in eighteen eligible studies, and a backward search of the references resulted in an additional five publications. Of the 23 studies, the majority were quantitative (74%), with fewer than half being theory driven (48%). Privacy fatigue was mainly conceptualized as a loss of control (74% of studies). Five categories of privacy fatigue antecedents were identified: privacy risk, privacy control and management, knowledge and information, individual differences, and privacy policy characteristics. This study highlights the need for greater attention to be paid to the methodological design and theoretical underpinning of future research. Quantitative studies should carefully consider the use of CB-SEM or PLS-SEM, should aim to increase the sample size, and should improve on analytical rigor. In addition, to ensure that the field matures, future studies should be underpinned by established theoretical frameworks. This review reveals a notable absence of privacy fatigue research when modeling the influence of privacy threats and invasions and their relationship with privacy burnout, privacy resignation, and increased self-disclosure. In addition, this review provides insight into theoretical and practical research recommendations that future privacy fatigue researchers should consider going forward. [ABSTRACT FROM AUTHOR]

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants.

The advent of intelligent technologies, notably Brain–Computer Interfaces (BCIs), has introduced novel privacy dilemmas. Ensuring judicious privacy transfer is imperative for the application of BCI technology and pivotal for fostering economic and technological progress. This study adopts privacy transfer as the research perspective and employs an ethical matrix as the research method. It establishes BCI users as the central core interests, with marketers, developers, and medical personnel as stakeholders. Departing from the binary opposition of public and private in traditional privacy theory, this article proposes ethical principles such as maximizing benefits, minimizing harm, and respecting independent decision-making power. It constructs a judgment matrix for the privacy transfer of BCIs, utilizing this matrix to identify ethical risks like privacy disclosure and hijacking. This study analyzes the reasons for risks, aiming to overcome dilemmas and construct an ethical matrix to explore privacy transfer boundary division methods suitable for BCI technology and tailored to different stakeholders.

For over a century, contact tracing has been an integral public health strategy for infectious disease control when there is no pharmaceutical treatment. Contact tracing for the coronavirus disease COVID-19 introduced a variety of automated methods deployed across several countries. The present paper examines privacy risk to infected persons and their physical contacts in contact tracing systems. Automated contact tracing systems implemented during the early months of COVID-19 are compared to conventional manual methods. Solove's taxonomy of privacy is applied to examine privacy risks in both conventional and automated contact tracing systems. As a method of epidemiological surveillance, all contact tracing systems inherently incur privacy risk. However, compared to conventional methods, automated contact tracing systems amplify privacy risk with pre-emptive data collection on all app users, regardless of exposure to an infectious disease; continuous, granular data collection on all users' location and proximity contacts; insecurities in proximity app technologies and interconnectivity; and in many cases, the use of centralised systems. Reducing these risk factors can reduce privacy harms, such as identification, distortion, secondary use, stigma, and social control. [ABSTRACT FROM AUTHOR]

The adaptation of the Internet of Medical Things (IoMT) has provided efficient and timely services and has transformed the healthcare industry to a great extent. Monitoring patients remotely and managing hospital records and data have become effortless with the advent of IoMT. However, security and privacy have become a significant concern with the growing number of threats in the cyber world, primarily for personal and sensitive user data. In terms of IoMT devices, risks appearing from them cannot easily fit into an existing risk assessment framework, and while research has been done on this topic, little attention has been paid to the methodologies used for the risk assessment of heterogeneous IoMT devices. This paper elucidates IoT, its applications with reference to in-demand sectors, and risks in terms of their types. By the same token, IoMT and its application area and architecture are explained. We have also discussed the common attacks on IoMT. Existing papers on IoT, IoMT, risk assessment, and frameworks are reviewed. Finally, the paper analyzes the available risk assessment frameworks such as NIST, ISO 27001, TARA, and the IEEE213-2019 (P2413) standard and highlights the need for new approaches to address the heterogeneity of the risks. In our study, we have decided to follow the functions of the NIST and ISO 270001 frameworks. The complete framework is anticipated to deliver a risk-free approach for the risk assessment of heterogeneous IoMT devices benefiting its users. [ABSTRACT FROM AUTHOR]

This study's primary goal is to examine the elements that affect customer loyalty and satisfaction with Portuguese telecommunications. Indeed, customer loyalty and satisfaction are crucial factors in guaranteeing the success and expansion of the services sector. Furthermore, it aims to include customers' privacy perceptions in a thorough model. A structured questionnaire was adapted from previous studies in the field, collecting a total of 357 valid responses. The suggested hypotheses were tested using multiple statistical techniques to assess the reliability and validity of the gathered data, culminating with path analysis through Structural Equation Modelling. The research results demonstrate that consumer loyalty is highly impacted by satisfaction. On the other hand, service quality significantly influences customer satisfaction, whereas trust and perceived value have a positive yet insignificant impact on this construct. Additionally, perceptions of privacy risk were found to affect customer trust positively and significantly. Considering that the data used for this analysis were collected exclusively in the Portuguese market, inferring the same findings in different countries should be made prudently. As this study only comprised of one of the perceived value dimensions, the results associated with this construct should also have that in mind. [ABSTRACT FROM AUTHOR]

Romance has never felt the same way since the inception of online dating. A match is made not in Heaven but in the Cloud. Swiping profiles could sweep someone's feet off. This work aims to assess the factors predicting the continuous use intention of online dating applications among young Malaysians through the lens of the UTAUT2 model and privacy risk. The research model was analyzed using Partial Least Squares Structural Equation Modelling per 282 valid responses from online daters in Malaysia. The results indicate the model's substantial predictive power with social influence, price value, and habit as significant drivers to continuous use intention of online dating applications. Habit has the strongest effect on the intention to use online dating applications continuously. This research adds to the body of knowledge on the behavioral intention of online dating applications and offers practical suggestions for online dating service providers to increase the likelihood of their continuous adoption. [ABSTRACT FROM AUTHOR]

Encrypted traffic classification plays a critical role in network management, providing appropriate Quality-of-Service and Network Intrusion Detection. Conventional port-based and deep packet inspection approaches cannot classify encrypted traffic effectively. Methods based on machine learning can classify encrypted traffic by extracting statistical features of the flow. However, they require manual extraction of features. Recent studies show that the approaches based on deep learning are compelling for the task. They can automatically learn raw traffic features without manual feature extraction. However, these studies still take the payload of encrypted traffic as the model input, which may cause privacy risks. Besides, a massive encrypted payload causes great storage pressure on traffic classification. In this paper, we propose a reliable encrypted traffic classification framework by only using the flow header called Only Header, which avoids privacy risks and achieves lightweight storage. Firstly, we introduce a twice segmentation mechanism to dilute the interference traffic and increase the weight of effective traffic. Then, we use capsule neural networks (CapsNet) to learn spatial and byte features of the flow header. The Only Header's effectiveness is compared with other methods using two public datasets, including ISCX VPN-nonVPN and ISCX Tor-nonTor datasets. The experimental results demonstrate that the Only Header outperforms the state-of-the-art encrypted traffic classification methods. [ABSTRACT FROM AUTHOR]

While today consumers benefit from personalised service offerings, they are also understandably concerned about the privacy risks generated by disclosing their personal information online. We know that such perceived risks in general shape behaviour, but we know little about what specific privacy risks obstruct the use of digital services, making it difficult to implement technologies that could mitigate these risks. Based on qualitative and quantitative studies involving over 1000 participants, we conceptualise and quantify a multidimensional perspective on privacy risks consisting of physical, social, resource‐related, psychological, prosecution‐related, career‐related and freedom‐related privacy risks. Our results explicate the prospects of distinguishing privacy risk dimensions by demonstrating how they are differently pronounced across contexts and how technology designs can be tailored to assuage them. Thus, our findings improve the understanding of context and service‐specific privacy risks, helping managers to adjust their digital offerings to mitigate users' privacy risk perceptions. [ABSTRACT FROM AUTHOR]

For the problem of poor performance of exciting membership inference attack (MIA) when facing the transfer learning model that is generalized, the MIA for the transfer learning model that is generalized was first systematically studied, the anomaly detection was designed to obtain vulnerable data samples, and MIA was carried out against individual samples.Finally, the proposed method was tested on four image data sets, which shows that the proposed MIA has great attack performance.For example, on the Flowers102 classifier migrated from VGG16 (pretraining with Caltech101), the proposed MIA achieves 83.15% precision, which reveals that in the environment of transfer learning, even without access to the teacher model, the MIA for the teacher model can be achieved by visiting the student model.

In traditional centralized Android malware classifiers based on machine learning, the training sample uploaded by users contains sensitive personal information, such as app usage and device security status, which will undermine personal privacy if used directly by the server. Federated-learning-based Android malware classifiers have attracted much attention due to their privacy-preserving and multi-party joint modeling. However, research shows that indirect privacy inferences from curious central servers threaten this framework. We propose a privacy risk evaluation framework, FedDroidMeter, based on normalized mutual information in response to user privacy requirements to measure the privacy risk in FL-based malware classifiers. It captures the essential cause of the disclosure of sensitive information in classifiers, independent of the attack model and capability. We performed numerical assessments using the Androzoo dataset, the baseline FL-based classifiers, the privacy-inferred attack model, and the baseline methodology of privacy evaluation. The experimental results show that FedDroidMeter can measure the privacy risks of the classifiers more effectively. Meanwhile, by comparing different models, FL, and privacy parameter settings, we proved that FedDroidMeter could compare the privacy risk between different use cases equally. Finally, we preliminarily study the law of privacy risk in classifiers. The experimental results emphasize the importance of providing a systematic privacy risk evaluation framework for FL-based malware classifiers and provide experience and a theoretical basis for studying targeted defense methods.

For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global "Ethical AI" movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods. Practitioner notesWhat is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters.Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries.A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics.What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics.It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline;It describes a repeatable framework for conducting ethical Learning Analytics.Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm.The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated.The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in early 2022). [ABSTRACT FROM AUTHOR]

Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of ‘user-environment-smart home product’. A total of 35 privacy risk scenarios of ‘component-threat-failure-model-incident’ combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems.

Online users are responsible for protecting their online privacy themselves: the mantra is custodiat te (protect yourself). Even so, there is a great deal of evidence pointing to the fact that online users generally do not act to preserve the privacy of their personal information, consequently disclosing more than they ought to and unwisely divulging sensitive information. Such self-disclosure has many negative consequences, including the invasion of privacy and identity theft. This often points to a need for more knowledge and awareness but does not explain why even knowledgeable users fail to preserve their privacy. One explanation for this phenomenon may be attributed to online privacy fatigue. Given the importance of online privacy and the lack of integrative online privacy fatigue research, this scoping review aims to provide researchers with an understanding of online privacy fatigue, its antecedents and outcomes, as well as a critical analysis of the methodological approaches used. A scoping review based on the PRISMA-ScR checklist was conducted. Only empirical studies focusing on online privacy were included, with nontechnological studies being excluded. All studies had to be written in English. A search strategy encompassing six electronic databases resulted in eighteen eligible studies, and a backward search of the references resulted in an additional five publications. Of the 23 studies, the majority were quantitative (74%), with fewer than half being theory driven (48%). Privacy fatigue was mainly conceptualized as a loss of control (74% of studies). Five categories of privacy fatigue antecedents were identified: privacy risk, privacy control and management, knowledge and information, individual differences, and privacy policy characteristics. This study highlights the need for greater attention to be paid to the methodological design and theoretical underpinning of future research. Quantitative studies should carefully consider the use of CB-SEM or PLS-SEM, should aim to increase the sample size, and should improve on analytical rigor. In addition, to ensure that the field matures, future studies should be underpinned by established theoretical frameworks. This review reveals a notable absence of privacy fatigue research when modeling the influence of privacy threats and invasions and their relationship with privacy burnout, privacy resignation, and increased self-disclosure. In addition, this review provides insight into theoretical and practical research recommendations that future privacy fatigue researchers should consider going forward.