Search Results (33)

The aim of this study was to explore the influence of a personal information agreement on customers’ information disclosure behavior. By integrating the existing privacy calculus theory, we sought to understand customer behavior in the context of encountering a personal information agreement and to provide insights into the efficacy of a company’s privacy policy. Our findings reveal that upon encountering a personal information agreement, customers perceive both a privacy retention period policy and privacy information sharing policy. We discovered that both policies significantly influence the perception of privacy benefits, but only the privacy information sharing policy impacts the perception of privacy risk. Furthermore, while privacy benefits were found to dictate information disclosure behavior in the context of a personal information agreement, perceptions of privacy risk did not significantly affect this behavior. Full article

The vehicle-to-vehicle (V2V) charging mode of charging stations solves the problem of users being unable to charge immediately due to the absence of charging piles during peak charging times. However, in blockchain-based V2V power transactions, attackers collect private information such as the payment address and transaction amount of electric vehicle owners through ledger information. This makes the relationship between electric vehicle owners and the charging behavior the object of inference attacks, resulting in user privacy disclosure and unfair trading. To solve these problems, we propose a communication scheme with privacy protection in V2V power transactions based on a linkable ring signature. We use a linkable ring signature algorithm to sign EV account addresses and payment information, ensuring the non-traceability of V2V transactions. In addition, we design a stealth address algorithm to avoid inferential attacks in V2V power transactions due to the exposure of the actual account address. The theoretical analysis proves the scheme’s security, and the experiment shows that the scheme has lower computing costs, so it is more suitable for V2V scenarios with limited computing resources. Full article

This research explores the psychological transition that occurs in dating app users from the Fear of Missing Out (FOMO) to the Joy of Missing Out (JOMO) using the Stressor–Strain–Outcome (SSO) model. An online survey of 410 Tinder users reveals that the FOMO significantly influences self-disclosure and social media stalking behaviors, which leads to user fatigue and eventually the JOMO. This survey also finds that privacy concerns play a moderating role in this process. In particular, the results show that a heightened FOMO increases self-disclosure and social media stalking, which intensifies fatigue and fosters the JOMO. Privacy concerns significantly modulate the relationship between the FOMO, fatigue, and the JOMO, thus playing a critical role in user interactions with dating apps. These insights help elucidate the socio-psychological behaviors of dating app users and can inform app design to reduce fatigue and enhance user well-being. Full article

The rapid advancement and integration of Artificial Intelligence (AI) in Internet of Things (IoT) technologies have raised significant concerns regarding privacy information disclosure. As AI-enabled IoT devices collect, process, and share vast amounts of personal data, it is crucial to understand the current state of research on this topic and identify areas for future investigation. This research systematically analyzed 38 peer-reviewed articles on privacy information disclosure in the AI-enabled IoT context. The analysis yielded pivotal themes pertinent to information disclosure in the IoT realm, encompassing facets such as consumer IoT adoption, personalized service, the commodification of information, external threats, vulnerability, innovation, regulation, behavioral patterns, trust, demographic considerations, user satisfaction, strategic marketing plans, and institutional reputation. This paper posits a combined summary research framework explaining user-centric information disclosure behavior in the IoT sphere in light of these disclosures. The insights presented cater to diverse stakeholders, including researchers, policymakers, and businesses, aiming for optimized AI-integrated IoT engagement while prioritizing privacy. Full article

The current research sought a comprehensive understanding about the consequences of information-sharing behavior on social media, given public concerns about privacy violations. We used a mixed-methods approach to investigate the influence of the self on “revealing” and emotional “healing” experiences online. Respondents completed a survey measuring sense of self and motivations for using social media, as well as revealing and healing attitudes and behavior. We conducted a principal component factor analysis on separate parts of the survey and ran Pearson correlations of the emerging factors. Qualitative data describing experiences of online self-disclosure were used to illustrate the correlational findings. The “revealing” factors contrasted adaptive with maladaptive and naïve posting. The sense of self, as well as motivations for social media use, influenced whether users engaged in destructive posting behaviors. The “healing” factors were associated with positive motivations for self-disclosure, seeking a supportive online community, and building resilience. Correlational data revealed that respondents with an insecure or asocial sense of self felt the greater need for online self-disclosure. Motivations to self-disclose online and experiences of “healing”, with the help of a supportive online community, depended on whether the sense of self was secure, insecure, or asocial. Full article

In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT. Full article

Continuous authentication enhances security by re-verifying a user’s validity during the active session. It utilizes data about users’ behavioral actions and contextual information to authenticate them continuously. Such data contain information about user-sensitive attributes such as gender, age, contextual information, and may also provide information about the user’s emotional states. The collection and processing of sensitive data cause privacy concerns. In this paper, we propose two efficient protocols that enable privacy-preserving continuous authentication. The contribution is to prevent the disclosure of user-sensitive attributes using partial homomorphic cryptographic primitives and reveal only the aggregated result without the explicit use of decryption. The protocols complete an authentication decision in a single unidirectional transmission and have very low communication and computation costs with no degradation in biometric performance. Full article

Safeguarding the data collected by smart meters is essential because the disclosure of this information may threaten the privacy of the consumer. By obtaining them, hackers can find out the behavior of the person and use that information for malicious purposes. Therefore, the anonymity of such information can prevent the occurrence of risks. Given the paramount significance of user privacy and data integrity, this paper primarily investigates the confidentiality, integrity, and anonymity of messages. This paper aims to develop a platform for determining dynamic pricing to coordinate supply and demand, thereby maximizing the efficiency of facilities. In the previous research, the operation center was not authenticated for the customer in the first step, and they also had a heavy computational cost. But this paper has endeavored to develop an efficient and comprehensive privacy-preserving solution for the smart electricity network. Also, it has tried to cover all the required security objectives by dealing with authenticity, confidentiality, and irrefutability. The method of the research is that two entities mutually authenticate each other and reach a key agreement so that if the operation center wants to send a control command, it can send control commands directly to the meter with less time complexity. The power company sends control commands and requests to the smart meters until the analyzed and collected energy consumption data are transmitted. The data aggregator node gathers the data from the meters. The results showed that the proposed method reduced the computational complexity and communication overhead to a satisfactory level and is also resistant to various attacks. Full article

What influences Information Communications and Technology (ICT) users’ privacy behavior? Several studies have shown that users state to care about their personal data. Contrary to that though, they perform unsafe privacy actions, such as ignoring to configure privacy settings. In this research, we present the results of an in-depth literature review on the factors affecting privacy behavior. We seek to investigate the underlying factors that influence individuals’ privacy-conscious behavior in the digital domain, as well as effective interventions to promote such behavior. Privacy decisions regarding the disclosure of personal information may have negative consequences on individuals’ lives, such as becoming a victim of identity theft, impersonation, etc. Moreover, third parties may exploit this information for their own benefit, such as targeted advertising practices. By identifying the factors that may affect SNS users’ privacy awareness, we can assist in creating methods for effective privacy protection and/or user-centered design. Examining the results of several research studies, we found evidence that privacy behavior is affected by a variety of factors, including individual ones (e.g., demographics) and contextual ones (e.g., financial exchanges). We synthesize a framework that aggregates the scattered factors that have been found in the literature to affect privacy behavior. Our framework can be beneficial to academics and practitioners in the private and public sectors. For example, academics can utilize our findings to create specialized information privacy courses and theoretical or laboratory modules. Full article

Background: With a massive population of internet users, China has witnessed a shift in the behavior of social media users towards the COVID-19 pandemic, transitioning from reticence to frequent sharing of information in response to changing circumstances and policy adjustments of the disease. This study aims to explore how perceived benefits, perceived risks, subjective norms, and self-efficacy influence the intentions of Chinese COVID-19 patients to disclose their medical history on social media, and thus to examine their actual disclosure behaviors. Methods: Based on the Theory of Planned Behavior (TPB) and Privacy Calculus Theory (PCT), a structural equation model was constructed to analyze the influence paths among perceived benefits, perceived risks, subjective norms, self-efficacy, and behavioral intentions to disclose medical history on social media among Chinese COVID-19 patients. A total of 593 valid surveys were collected via a randomized internet-based survey, which constituted a representative sample. Firstly, we used SPSS 26.0 to conduct reliability and validity analyses of the questionnaire, as well as the tests of demographic differences and correlations between variables. Next, Amos 26.0 was employed to construct and test the model fit degree, identify the relationships among latent variables, and conduct path tests. Results: Our findings revealed the following: (1) There were significant gender differences in the self-disclosure behaviors of medical history on social media among Chinese COVID-19 patients. (2) Perceived benefits had a positive effect on self-disclosure behavioral intentions (β = 0.412, p < 0.001); perceived risks had a positive effect on self-disclosure behavioral intentions (β = 0.097, p < 0.05); subjective norms had a positive effect on self-disclosure behavioral intentions (β = 0.218, p < 0.001); self-efficacy had a positive effect on self-disclosure behavioral intentions (β = 0.136, p < 0.001). (3) Self-disclosure behavioral intentions had a positive effect on disclosure behaviors (β = 0.356, p < 0.001). Conclusions: Our study, by integrating TPB and PCT to examine the influencing factors of the self-disclosure behaviors among Chinese COVID-19 patients on social media, found that perceived risks, perceived benefits, subjective norms, and self-efficacy had a positive influence on the self-disclosure intentions of Chinese COVID-19 patients. We also found that self-disclosure intentions, in turn, positively influenced disclosure behaviors. However, we did not observe a direct influence of self-efficacy on disclosure behaviors. Our study provides a sample of the application of TPB in the context of social media self-disclosure behavior among patients. It also introduces a novel perspective and potential approach for individuals to address the feelings of fear and shame related to illness, particularly within the context of collectivist cultural values. Full article

Reporting helps to combat illegal activities and deters lawbreakers and potential lawbreakers. From ancient times to the present, public authorities have usually rewarded effective reporting information to build harmonious societies. In this process, protecting the privacy of the whistleblower is a very important issue. Existing blockchain-based anonymous reporting solutions help solve the problem of insufficient anonymity in traditional reporting solutions, but they do not address the issue of hiding the reporting behavior. The disclosure of reporting behavior may alert offenders in advance and negatively impact case handling. This paper proposes an anonymous and covert reporting scheme and rewarding mechanism based on blockchain, which realizes the covertness of the reporting behavior while protecting the privacy of the whistleblower. The proposed scheme uses ring signature and derived address technology to ensure anonymity and achieves covertness by embedding information in the ring signature based on the idea of covert communication. Theoretical analysis proves that the proposed scheme has covertness, anonymity, and unforgeability properties. Experiments show that the proposed scheme takes only 0.08 s to upload data and 0.07 s to verify while achieving covertness. Full article

This study aimed to examine the effects of perceived privacy risks and benefits on the online privacy protection behaviors of Chinese teens, with information privacy concerns treated as a mediator variable. The questionnaire survey data (N = 1538) were collected from teens in seven provinces of Mainland China and were analyzed using a structural equation model (SEM). This study found that the effects of teens’ perceived privacy benefits on their information privacy concerns and online privacy protection behaviors are insignificant, but the effects of teens’ perceived privacy risk on their online privacy protection behaviors are significantly positive. Additionally, information privacy concerns significantly mediated the effects of perceived privacy risk on the online privacy protection behaviors of Chinese teens. Full article

Drawing from the tension between a company’s desire for customer information to tailor experiences and a consumer’s need for privacy, this study aims to test the effect of two information disclosure nudges on users’ information disclosure behaviors. Whereas previous literature on user-chatbot interaction focused on encouraging and increasing users’ disclosures, this study introduces measures that make users conscious of their disclosure behaviors to low and high-sensitivity questions asked by chatbots. A within-subjects laboratory experiment entailed 19 participants interacting with chatbots, responding to pre-tested questions of varying sensitivity while being presented with different information disclosure nudges. The results suggest that question sensitivity negatively impacts users’ information disclosures to chatbots. Moreover, this study suggests that adding a sensitivity signal—presenting the level of sensitivity of the question asked by the chatbot—influences users’ information disclosure behaviors. Finally, the theoretical contributions and managerial implications of the results are discussed. Full article

Due to strong survivability and flexible scheduling, multi-UAV (Unmanned Aerial Vehicle)-assisted communication networks have been widely used in civil and military fields. However, the open accessibility of wireless channels brings a huge risk of privacy disclosure to UAV-based networks. This paper considers a multi-UAV-assisted covert communication system based on Wireless Powered Communication (WPC) and Clustered-Non-Orthogonal-Multiple-Access (C-NOMA), aiming to hide the transmission behavior between UAVs and legitimate ground users (LGUs). Specifically, the UAVs serve as aerial base stations to provide services to LGUs, while avoiding detection by the ground warden. In order to improve the considered covert communication performance, the average uplink covert rate of all clusters in each slot is maximized by jointly optimizing the cluster scheduling variable, subslot allocation, LGU transmit power and multi-UAV trajectory subject to covertness constraints. The original problem is a mixed integer non-convex problem, which are typically difficult to solve directly. To solve this challenge, this paper decouples it into four sub-problems and solves the sub-problems by alternating iterations until the objective function converges. The simulation results show that the proposed multi-UAV-assisted covert communication scheme can effectively improve the average uplink covert rate of all clusters compared with the benchmark schemes. Full article

Genomic screening programs have potential to benefit individuals who may not be clinically ascertained, but little is known about the psychological impact of receiving genetic results in this setting. The current study sought to further the understanding of individuals’ psychological response to receiving an actionable genetic test result from genomic screening. Telephone surveys were conducted with patient-participants at 6 weeks and 6 months post genetic result disclosure between September 2019 and May 2021 and assessed emotional response to receiving results via the FACToR, PANAS, and decision regret scales. Overall, 354 (29.4%) study participants completed both surveys. Participants reported moderate positive emotions and low levels of negative emotions, uncertainty, privacy concern, and decision regret over time. There were significant decreases in negative emotions (p = 0.0004) and uncertainty (p = 0.0126) between time points on the FACToR scale. “Interested” was the highest scoring discrete emotion (T1 3.6, T2 3.3, scale 0–5) but was significantly lower at 6 months (<0.0001). Coupled with other benefits of genomic screening, these results of modest psychological impact waning over time adds support to clinical utility of population genomic screening programs. However, questions remain regarding how to elicit an emotional response that motivates behavior change without causing psychological harm. Full article