A Model of Perception of Privacy, Trust, and Self-Disclosure on Online Social Networks

Zlatolas, Lili Nemec; Welzer, Tatjana; Hölbl, Marko; Heričko, Marjan; Kamišalić, Aida

doi:10.3390/e21080772

Cited by 32 publications

(33 citation statements)

References 70 publications

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“…Therefore, there is some evidence that for newer IT services like those provided by IoT or the VA devices, having greater control over the personal data not always transforms to a reduced perception of privacy risks. Consistent with previous works [78][79][80], this study also confirms the relationship between trust and perceived risks.VA services are new, and for it to succeed the users must trust all the stakeholders who are involved in it.…”

Section: Major Findingssupporting

confidence: 92%

“…Trust has been addressed by a number of prior studies and is generally viewed as a type of belief by the users where they can confide on certain entities to protect their personal information [47]. Therefore, perceived trust is positively related to reduce the privacy risks of personal information disclosure [78][79][80]. Accordingly, the following hypothesis: Hypothesis 6 A greater extent of perceived trust decreases the level of perceived risks of personal information disclosure in the VA realm.…”

Section: The Second Channel: Antecedents Of Perceived Risksmentioning

confidence: 99%

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Personal Information Disclosure via Voice Assistants: The Personalization–Privacy Paradox

2020

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The purpose of this research is to examine the factors affecting the willingness to disclose personal information based upon the privacy calculus framework and its relation to the continued usage of the Voice Assistant (VA) devices. The information disclosure is analyzed from a dual channel benefit/risk perspective through the calculus lens. Particularly, the joint effects of the two independent paths of benefits and risks that can induce the desire to disclose personal information, along with their various antecedents are proposed and empirically tested. Data are collected from 427 respondents using Amazon Mechanical Turk (MTurk) as the platform and analyzed using a Maximum Likelihood Structural Equation Modelling (ML-SEM) technique. The analysis shows that personalized services, perceived enjoyment and perceived complementarity influence the perceived benefits, which in turn positively affects the personal information disclosure. Perceived severity and perceived control are found to have a positive effect, while perceived trust has a negative effect on the perceived risks, respectively, which in turn is negatively associated with the personal information disclosure. Compared to the perceived risks, the effect of perceived benefits on the information disclosure is found to be stronger, which motivates the users to continuously use the services of the VA devices. This work extends the current literatures on privacy concerns to the users' intention of disclosing their personal information by proposing and empirically testing six hypotheses as a part of the research model. Finally, the research implications are discussed, and suggestions are provided.

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Section: Major Findingssupporting

confidence: 92%

Section: The Second Channel: Antecedents Of Perceived Risksmentioning

confidence: 99%

Personal Information Disclosure via Voice Assistants: The Personalization–Privacy Paradox

2020

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“…However, knowledge of how to achieve data protection compliance is still quite limited from the point of view of software development teams. Thus, data privacy has become a significant concern to project managers and software development teams, mainly due to various incidents related to unauthorized data exploitation, misuse of personal information stored on social media sites, and disclosure of personal information to third parties without the consent of the data owners [1,2]. In addition, authors of different papers report the lack of understanding or knowledge in software privacy by the developers, and, in some cases, they do not know how to develop privacy-sensitive systems [3].…”

Section: Introductionmentioning

confidence: 99%

Perceptions of ICT Practitioners Regarding Software Privacy

Canedo

Calazans

Masson

et al. 2020

Entropy

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During software development activities, it is important for Information and Communication Technology (ICT) practitioners to know and understand practices and guidelines regarding information privacy, as software requirements must comply with data privacy laws and members of development teams should know current legislation related to the protection of personal data. In order to gain a better understanding on how industry ICT practitioners perceive the practical relevance of software privacy and privacy requirements and how these professionals are implementing data privacy concepts, we conducted a survey with ICT practitioners from software development organizations to get an overview of how these professionals are implementing data privacy concepts during software design. We performed a systematic literature review to identify related works with software privacy and privacy requirements and what methodologies and techniques are used to specify them. In addition, we conducted a survey with ICT practitioners from different organizations. Findings revealed that ICT practitioners lack a comprehensive knowledge of software privacy and privacy requirements and the Brazilian General Data Protection Law (Lei Geral de Proteção de Dados Pessoais, LGPD, in Portuguese), nor they are able to work with the laws and guidelines governing data privacy. Organizations are demanded to define an approach to contextualize ICT practitioners with the importance of knowledge of software privacy and privacy requirements, as well as to address them during software development, since LGPD must change the way teams work, as a number of features and controls regarding consent, documentation, and privacy accountability will be required.

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“…Recently, trust has played a vital role to help preserve the privacy and to enhance the security of social networks [27]- [30]. Zlatolas et al [31] proposed a model for privacy issues, trust and self-disclosure in online social networks (OSN) by considering various factors such as privacy risk, privacy value, trust on Facebook, self disclosure, privacy control and privacy concerns. They validate their model by using a survey attended by 602 respondents.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Trust and Reputation Model for Fog-Based IoT

et al. 2020

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Trust and reputation are important terms whether the communication is Humans-to-Human (H2H), Human-Machine-Interaction (HMI) or Machine-to-Machine (M2M). As Cloud computing and the internet of things (IoT) bring new innovations, they also cause various security and privacy issues. As numerous devices are continuously integrating as a core part of IoT, it is necessarily important to consider various security issues such as the trustworthiness of a user or detection of a malicious user. Moreover, fog computing also known as edge computing is revolutionizing the Cloud-based IoT by providing the Cloud services at the edge of the network, which can provide aid in overcoming security, privacy and trust issues. In this work, we propose a context-aware trust evaluation model to evaluate the trustworthiness of a user in a Fog based IoT (FIoT). The proposed approach uses a context-aware multi-source trust and reputation based evaluation system which helps in evaluating the trustworthiness of a user effectively. Further, we use context-aware feedback and feedback crawler system which helps in making trust evaluation unbiased, effective and reliable. Furthermore, we introduce monitor mode for malicious/untrustworthy users, which helps in monitoring the behavior and trustworthiness of a user. The proposed approach uses several tunable factors, which can be tuned based on the system's requirements. The simulations and results indicate that our approach is effective and reliable to evaluate the trustworthiness of a user.

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A Model of Perception of Privacy, Trust, and Self-Disclosure on Online Social Networks

Cited by 32 publications

References 70 publications

Personal Information Disclosure via Voice Assistants: The Personalization–Privacy Paradox

Personal Information Disclosure via Voice Assistants: The Personalization–Privacy Paradox

Perceptions of ICT Practitioners Regarding Software Privacy

Context-Aware Trust and Reputation Model for Fog-Based IoT

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