Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure

Perentis, Christos; Vescovi, Michele; Leonardi, Chiara; Moiso, Corrado; Musolesi, Mirco; Pianesi, Fabio; Lepri, Bruno

doi:10.1145/3017431

Cited by 15 publications

(7 citation statements)

References 39 publications

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“…(2016) Proceedings of the VLDB Endowment Computer Science ● ○ Mun et al. (2014) ACM Transactions on Sensor Networks Computer Networks & Communications ● ○

Nguyen (2020) Internet of Things Internet of Things Applications ● ○ Perentis et al. (2017) ACM Transactions on Internet Technology Computer Networks and Communications ○ ● Qi et al.…”

Section: Methodology Developmentmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

158

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Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

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“…(2016) Proceedings of the VLDB Endowment Computer Science ● ○ Mun et al. (2014) ACM Transactions on Sensor Networks Computer Networks & Communications ● ○

Nguyen (2020) Internet of Things Internet of Things Applications ● ○ Perentis et al. (2017) ACM Transactions on Internet Technology Computer Networks and Communications ○ ● Qi et al.…”

Section: Methodology Developmentmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

158

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“…A study was conducted by Perentis et al in 2017 in Italy including 63 people with various gender and age, jobs, and education which results that social interaction inferred by mobile phones have a significant impact on willingness (Perentis et al 2017). An online empirical survey was conducted among 913 respondents; they found that both monetary rewards and social rewards positively affect self-disclosure intention, whereas only social rewards positively affect self-disclosure honesty (Wang et al 2017).…”

Section: Literature Review:-mentioning

confidence: 99%

The Factors Affecting Personal Information Disclosure and Usage Continuance Intention on Mobile Social Networking Services

Ghaderian¹,

Hussain²

2021

IJAR

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Human life has been greatly changed by the explosive growth of the internet and social media. This paper explores the factors affecting the individual information disclosure and usage continuance intention on mobile social networking services. Therefore, this study is aimed to test hypothesesrelated to the proposed calculus theory rather than generate new hypotheses. Therefore, the present research study is deductive in nature and thus is consistent with the positivism paradigm. The present study intends to adopt a quantitative research strategy using a structured survey questionnaire. Questionnaires are prepared on a five-pointLikert scale and distributed to 180 individuals in the Klang Valley in Malaysia. Indeed, the opinions of the respondents are different from each other a non-probability sampling tool has been used. The results of the study were analyzed by Pearson correlation tests and indicate that several parameters are highly significant, including privacy concerns, perceived risk, information sensitivity, reliable beliefs, perceived usefulness, and relationships for disclosure. The information mentioned that the respondents notice in the disclosure of information and their intention to use it. Three factors, such as privacy concern, perceived risk, and information sensitivity, are associated negatively with significance towards the information discloser among the users of the mobile apps in social networks in Malaysia. The other three factors, such as trusting beliefs, perceived usefulness, and relationship building, are positively associated with information disclosure.

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“…Some recent works [33,34] have also investigated the factors affecting users choices toward the disclosure of their personal data. Their works are different from our work, which considered the factors from the perspectives of data features and sampling rates.…”

Section: Related Workmentioning

confidence: 99%

An information-aware visualization for privacy-preserving accelerometer data sharing

Xiao

Zhao

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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In the age of big data, plenty of valuable sensing data have been shared to enhance scientific innovation. However, this may cause unexpected privacy leakage. Although numerous privacy preservation techniques, such as perturbation, encryption, and anonymization, have been proposed to conceal sensitive information, it is usually at the cost of the application utility. Moreover, most of the existing works did not distinguished the underlying factors, such as data features and sampling rate, which contribute differently to utility and privacy information implied in the shared data. To well balance the application utility and privacy leakage for data sharing, we utilize mutual information and visualization techniques to analyze the impact of the underlying factors on utility and privacy, respectively, and design an interactive visualization tool to help users identify the appropriate solution to achieve the objectives of high application utility and low privacy leakage simultaneously. To illustrate the effectiveness of the proposed scheme and tool, accelerometer data collected from mobile devices have been adopted as an illustrative example. Experimental study has shown that feature selection and sampling frequency play dominant roles in reducing privacy leakage with much less reduction on utility, and the proposed visualization tool can effectively recommend the appropriate combination of features and sampling rates that can help users make decision on the trade-off between utility and privacy.

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Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure

Cited by 15 publications

References 39 publications

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

The Factors Affecting Personal Information Disclosure and Usage Continuance Intention on Mobile Social Networking Services

An information-aware visualization for privacy-preserving accelerometer data sharing

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