Privacy Perspective Research Articles

Urgency of Human Rights Protection in the Digital Age in the Perspective of Data Security and Privacy

This research aims to provide a better understanding of current technological developments, how the challenges of the digital era can affect human rights and how the law can be used as a tool to protect these rights. This research also explains cases of human rights violations related to data security and privacy, as well as looking for legal solutions that can help maintain a balance between technological progress and human rights. This research was prepared using a literacy approach or literature review using various secondary library sources to answer all problems in the research carried out.

Security and Privacy Perspectives of People Living in Shared Home Environments

Security and privacy (S&P) perspectives of people in a multi-user home are a growing area of research, with many researchers reflecting on the complicated power imbalance and challenging access control issues of the devices involved. However, these studies primarily focused on the multi-user scenarios in traditional family home settings, leaving other types of multi-user home environments, such as homes shared by co-habitants without a familial relationship, under-studied. This paper closes this research gap via quantitative and qualitative analysis of results from an online survey and qualitative content analysis of sampled online posts on Reddit. The study explores the complex roles of shared home users, which depend on various factors unique to the shared home environment, e.g., who owns what home devices, how home devices are used by multiple users, and more complicated relationships between the landlord and people in the shared home and among co-habitants. Half (50.7%) of our survey participants thought that devices in a shared home are less secure than in a traditional family home. This perception was found statistically significantly associated with factors such as the fear of devices being tampered with in their absence and (lack of) trust in other co-habitants and their visitors. We observed cyber-physical threats being a prominent topic discussed in Reddit posts. Our study revealed new user types and user relationships in a multi-user environment such as ExternalPrimary-InternalPrimary while analysing the landlord and shared home resident relationship with regard to shared home device use. Based on the results of the online survey and the Reddit data, we propose a threat actor model for shared home environments, which has a focus on possible malicious behaviours of current and past co-habitants of a shared home, as a special type of insider threat in a home environment. We also recommend further research to understand the complex roles co-habitants can play in navigating and adapting to a shared home environment's security and privacy landscape.

A Privacy Perspective in Adopting Smart Contract Applications for Healthcare

ABSTRACT Smart contract applications are currently implemented in healthcare, but further research is needed to investigate their adoption by embracing a privacy perspective. This study fills this research gap by exploring smart contracts’ adoption in healthcare through a privacy calculus model integrated with technical, legal, and social factors. The authors explore two specific applications: smart contracts implementing immune certificates (SC-ICs) to fight a healthcare crisis and smart contracts encoding and storing electronic health records (SC-EHRs) in the absence of such a crisis. Data are collected through a survey, and partial least square modeling is used to test the research hypotheses. The results provide significant contributions for academics and practitioners by showing how the influence of technical, legal, and social factors on privacy evaluations and privacy calculus evaluations themselves differ in the case of different applications of the same technology, i.e. SC-ICs and SC-EHRs because of the peculiar healthcare scenario.

Trustworthy human computation: a survey

Human computation is an approach to solving problems that prove difficult using AI only, and involves the cooperation of many humans. Because human computation requires close engagement with both “human populations as users” and “human populations as driving forces,” establishing mutual trust between AI and humans is an important issue to further the development of human computation. This survey lays the groundwork for the realization of trustworthy human computation. First, the trustworthiness of human computation as computing systems, that is, trust offered by humans to AI, is examined using the RAS (reliability, availability, and serviceability) analogy, which define measures of trustworthiness in conventional computer systems. Next, the social trustworthiness provided by human computation systems to users or participants is discussed from the perspective of AI ethics, including fairness, privacy, and transparency. Then, we consider human–AI collaboration based on two-way trust, in which humans and AI build mutual trust and accomplish difficult tasks through reciprocal collaboration. Finally, future challenges and research directions for realizing trustworthy human computation are discussed.

Perceptions of the Quality of HIV and AIDS-related Communication Materials among Key Population

Exploring the quality of communication materials from the perspectives of the target audiences is essential for the effectiveness of HIV and AIDS prevention programmes. In this context, a descriptive phenomenological study was carried out in Banke district of Nepal to explore the audiences’ perceptions of the quality of HIV and AIDS-related communication materials. In-depth interviews were carried out with nine participants selected through snowball sampling from sexual minority people and female sex workers. The participants were partially satisfied with the quality of the communication materials from the perspectives of attractiveness, credibility, empowerment, practical application, behaviour change and privacy. They were not satisfied with their quality from the perspectives of readability, sufficiency, comprehensibility and access. This study suggests that communication materials including precise and illustrative messages with familiar pictures, symbols and language should be easily available to the audiences maintaining privacy.

Disclosing Economists’ Privacy Perspectives: A Survey of American Economic Association Members’ Views on Differential Privacy and the Usability of Noise-Infused Data

Disclosing Economists’ Privacy Perspectives: A Survey of American Economic Association Members’ Views on Differential Privacy and the Usability of Noise-Infused Data

Horizontal Federated Recommender System: A Survey

Due to underlying privacy-sensitive information in user-item interaction data, the risk of privacy leakage exists in the centralized-training recommender system (RecSys). To this issue, federated learning, a privacy-oriented distributed computing paradigm, is introduced and promotes the crossing field “Federated Recommender System (FedRec).” Regarding data distribution characteristics, there are horizontal, vertical, and transfer variants, where horizontal FedRec (HFedRec) occupies a dominant position. User devices can personally participate in the horizontal federated architecture, making user-level privacy feasible. Therefore, we target the horizontal point and summarize existing works more elaborately than existing FedRec surveys. First, from the model perspective, we group them into different learning paradigms (e.g., deep learning and meta learning). Second, from the privacy perspective, privacy-preserving techniques are systematically organized (e.g., homomorphic encryption and differential privacy). Third, from the federated perspective, fundamental issues (e.g., communication and fairness) are discussed. Fourth, each perspective has detailed subcategories, and we specifically state their unique challenges with the observation of current progress. Finally, we figure out potential issues and promising directions for future research.

Explainable AI and Blockchain for Metaverse: A Security and Privacy Perspective

Explainable AI and Blockchain for Metaverse: A Security and Privacy Perspective

Application of Kimi Intelligent Assistant in the Teaching of Water Pollution Control Engineering Course

With the rapid development of artificial intelligence technology, its application in the field of education is becoming increasingly widespread. This paper introduces the functional features of the Kimi Intelligent Assistant and elaborates on its application in the teaching of Water Pollution Control Engineering course from aspects such as assisting in lesson plan design, creating mind maps, answering questions, process design, exercise compilation, and extracurricular extended learning. Challenges and countermeasures of introducing Kimi Intelligent Assistant into the course teaching are analyzed from the perspectives of technology integration, teacher training, student acceptance, data security and privacy, cost, and curriculum content updates. The paper also provides a prospect for the further application of the Kimi Intelligent Assistant, suggesting that it is expected to become an important tool in promoting the technological development of future environmental engineering education.

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results

We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analytics tasks and graph learning tasks with GNNs. We conclude our work with a discussion of open questions and potential directions for further research in this area.

The effects of negative social media connotations on subjective wellbeing of an ageing population: A stressor-strain-outcome perspective.

In recent years, users' privacy concerns and reluctance to use have posed a challenge for the social media and wellbeing of its users. There is a paucity of research on elderly users' negative connotations of social media and the way these connotations contribute to developing passive behaviour towards social media use, which, in turn, affects subjective wellbeing. To address this research vacuum we employed the stressor-strain-outcome (SSO) approach to describe the evolution of passive social media use behaviour from the perspective of communication overload, complexity, and privacy. We conceptualized subjective wellbeing as a combination of three components-negative feelings, positive feelings, and life satisfaction. Negative and positive feelings were used to derive an overall affect balance score that fluctuates between 'unhappiest possible' and 'happiest possible'. The proposed research framework was empirically validated through 399 valid responses from elderly social media users. Our findings reveal that communication overload and complexity raise privacy concerns among social media users, which leads to passive usage of social media. This passive social media use improved the subjective wellbeing favourably by lowering negative feelings and raising positive feelings and life satisfaction. The findings also revealed that respondents' overall affect balance leans towards positive feelings as a consequence of passive social media use. This study contributes to the field of technostress by illuminating how the SSO perspective aid the comprehension of the way passive social media use influences the subjective wellbeing of its users.

Blockchain based mobile communication data privacy protection algorithm for cellular networks

The rapid development of mobile communication technology not only brings great convenience to users, but also brings the risk of user data privacy leakage. Due to the broadcasting nature of mobile communication transmission, open wireless interfaces have become a security vulnerability for mobile devices such as mobile phones, which can be easily eavesdropped. This article studied a data privacy protection algorithm suitable for mobile communication in cellular networks based on the anonymous mechanism of blockchain. This article first analyzes the overall framework and anonymity technology of blockchain data from the perspective of privacy and queryability. Then, based on blockchain technology, consistency mechanisms, privacy control, and access rights management are designed. Finally, mobile communication data privacy protection algorithms are designed and implemented through data encryption and verification. The latency of transactions under different task volumes and throughput at different nodes were analyzed to verify the reliability of several anonymous mechanisms in cellular network mobile communication data privacy protection in blockchain. Based on the experimental results, it was concluded that the reliability range of CryptoNote and the zero coin and zero currency protocol mechanism in the specified nodes was between 92 and 99%. This article utilized blockchain technology to distribute mobile communication data in nodes and achieve decentralized data transmission, thus protecting the privacy of wireless network communication data. By analyzing the reliability of anonymous mechanisms based on blockchain in mobile communication nodes, it was concluded that this method had certain research value.

Textile and apparel enterprises sustainable development strategy: focus on ecologic monitoring strategic communications

Strategy determines the success of the long-term development of any enterprise. In the context of sustainability, the consumer wants to be aware of what strategic principles the company adheres to, including questions about the use of environmental materials, technological innovation, corporate social responsibility, and other aspects. All these aspects should be reflected in the sustainability strategy, which, like any document, is in closed mode. In order to broadcast certain business practices in the field of sustainable development, any brand of light industry and fashion industry needs a system of strategic communications. This paper analyzes the corporate experiences of famous brands in the industry, examining their mission, vision, principles, ideology, philosophy and so on. The study has created a unique model that can help brands with sustainability information policy from the perspective of privacy in terms of partial disclosure of their sustainability strategy. In the future, the study can be extended to quantitative and qualitative analysis of the effects of changes in the communication system.

Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize the information for activity tracking and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms and allow users to share activities on such platforms or even with other social network platforms. To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques and achieve a prediction success rate ranging from 59.59% to 99.80%. The findings are alarming, highlighting that sharing elevation information may have significant location privacy risks.

Ensuring data confidentiality and integrity in edge computing environments : A security and privacy perspective

This study examines, from a security and privacy standpoint, the significant obstacles to maintaining data integrity and confidentiality in edge computing contexts. Data security at the edge becomes critical as more and more edge devices proliferate and generate large volumes of sensitive data. The review of the literature looks at edge computing security problems that currently exist and discusses fixes to protect data integrity and confidentiality. The article explores various vulnerabilities, emphasizing the necessity for strong security systems, including data exposure risks and integrity concerns. It examines secure communication protocols, access control methods, and encryption techniques as crucial risk-reduction instruments. Anonymization and differential privacy are two privacy-preserving techniques that are investigated to find a middle ground between user privacy and data value. The article provides real-world insights through case studies and actual implementations, and it lays out metrics and evaluation criteria for determining how successful security measures are. Finally, the report highlights the need of addressing data security in changing edge computing settings and offers future directions and research difficulties.

Security and Privacy Requirements for the Metaverse: A Metaverse Applications Perspective

The metaverse overcomes the time and space constraints of the real world and expands the physical world based on the Internet of Things, virtual reality, and digital twins. For this reason, interest in metaverse applications has recently increased because they can provide immersion and varied experiences similar to those in the real world. However, some security and privacy incidents are being encountered by these applications. Furthermore, users in the metaverse are concerned about personal data breaches, but metaverse studies on security and privacy are lacking. Thus, this study aims to propose security and privacy requirements for secure metaverse applications. To achieve this aim, we first analyze the existing metaverse-related studies and international standards from the perspective of security and privacy. We then present the security and privacy threats to the metaverse, which are identified by considering the characteristics of the applications. Finally, we propose security and privacy requirements and countermeasures to minimize these threats.

A study of innovations in legal governance with respect to the safety of artificial intelligence

Abstract This paper aims to promote the safe development of artificial intelligence and improve legal policies. Combined with the cluster analysis algorithm, it analyzes the safety risks as well as legal defects of artificial intelligence. The Laplace matrix is derived using the similarity matrix, and the feature vector space is constructed by analyzing the associated features of artificial intelligence safety. Combining the spectral clustering algorithm, legal assessment indexes for artificial intelligence safety were constructed. The modular metric value method is utilized to assess the clustering effect of laws on the safety of artificial intelligence. Analyzing the security risks of artificial intelligence, improved legal policies are proposed from the perspective of technology and privacy. The results show that the effect of improving privacy protection policy on privacy protection is 0.85, and the effect of clarifying subject rights is 0.9. The introduction of laws should consider social ethics, and the effect degree of ethical principles is 0.75. Clarifying subject rights can help avoid technological risks to a certain extent, and improving privacy protection policies can help protect users’ privacy.

Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility

Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies varepsilon-differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.

Elicitation and Empathy with AI-enhanced Adaptive Assistive Technologies (AATs)

Efforts to include people with disabilities in design education are difficult to scale, and dynamics of participation need to be carefully planned to avoid putting unnecessary burdens on users. However, given the scale of emerging AI-enhanced technologies and their potential for creating new vulnerabilities for marginalized populations, new methods for generating empathy and self-reflection in technology design students (as the future creators of such technologies) are needed. We report on a study with Information Systems graduate students where they used a participatory elicitation toolkit to reflect on two cases of end-user privacy perspectives towards AI-enhanced tools in the age of surveillance capitalism: their own when using tools to support learning, and those of older adults using AI-enhanced adaptive assistive technologies (AATs) that help with pointing and typing difficulties. In drawing on the experiences of students with intersectional identities, our exploratory study aimed to incorporate intersectional thinking in privacy elicitation and further understand its role in enabling sustainable, inclusive design practice and education. While aware of the risks to their own privacy and the role of identity and power in shaping experiences of bias, students who used the toolkit were more sanguine about risks faced by AAT users—assuming more data equates to better technology. Our tool proved valuable for eliciting reflection but not empathy.

Integrating blockchain technology and cloud services in healthcare: a security and privacy perspective

Integrating blockchain technology and cloud services in healthcare: a security and privacy perspective