Big data privacy issues in public social media

Smith, Matthew; Szongott, Christian; Henne, Benjamin; Voigt, G.

doi:10.1109/dest.2012.6227909

Cited by 157 publications

(70 citation statements)

References 10 publications

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“…However, the use of social media in the healthcare environment poses some challenges, which include threat to privacy and the need to continuously monitor and manage where and when possible (Katal et al 2013). The emergent threats to user online privacy comes from other users' social media, which makes this threat particularly bad, as the victims are often not involved in the process of uploading the content and as a result cannot take any pre-emptive cautions, in that the amount of data being uploaded varies so much that it cannot be detected manually (Smith et al 2012).…”

Section: Big Data and Social Media In Healthcarementioning

confidence: 99%

Integration of social media with healthcare big data for improved service delivery

Mgudlwa¹,

Iyamu²

2018

S. Afr. j. inf. manag.

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Background: In the last decade, social media users across the world have crossed 1 billion, making it one of the fastest growing sources of big data. Also, people needing healthcare continue to increase in every society. Through accessibility, communication and interaction between health practitioners and patients, this type of ever-growing, social media subscriber–based platform can be of significant use in improving healthcare delivery to society. However, users encounter serious challenges in their attempts to make use of social media and big data for health-related services. The challenges are primarily caused by factors such as integration, complexity, security and privacy. The challenges are mainly owing to the sensitive nature of the healthcare environment, as a result of personalisation and privacy of information. Objectives: The objectives of the study were to examine and gain a better understanding of the complexities that are associated with the use of social media and healthcare big data, through influencing factors, and to develop a framework that can be used to improve health-related services to the patients. Methods: The interpretivist approach was employed, within which qualitative data were collected. This included documents and existing literature in the areas of social media and healthcare big data. To have a good spread of both previous and current state of events within the phenomena being studied, literature published between 2006 and 2016 were gathered. The data were interpretively analysed. Results: Based on the analysis of the data, factors of influence were found, which were used to develop a model. The model illustrates how the factors of influence can enable and at the same time constrain the use of social media for healthcare services. The factors were interpreted from which a framework was developed. The framework is intended to guide integration of social media with healthcare big data through which service delivery to patients can be improved. Conclusion: This study can be used to guide integration of social media with healthcare big data by health facilities in the communities. The study contributes to healthcare workers’ awareness on how social media can possibly be used to improve the services that they provide to the needy. Also, the study will benefit information systems and technologies and academic domains, particularly from the health services’ perspective.

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Section: Big Data and Social Media In Healthcarementioning

confidence: 99%

Integration of social media with healthcare big data for improved service delivery

Mgudlwa¹,

Iyamu²

2018

S. Afr. j. inf. manag.

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“…The increasing use of online social networks also causes privacy issues, and sensitive information is usually either publicly provided or uploaded by other people or friends [14], [15]. Authors in [1] showed that, with a small piece of seed information, attackers can search local database or query web search engine, to launch re-identification attacks and crossdatabase aggregation.…”

Section: Related Workmentioning

confidence: 99%

Role-Playing Game for Studying User Behaviors in Security: A Case Study on Email Secrecy

Xu¹,

Yao²,

Pérez-Quiñones³

et al. 2014

Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Abstract-Understanding the capabilities of adversaries (e.g., how much the adversary knows about a target) is important for building strong security defenses. Computing an adversary's knowledge about a target requires new modeling techniques and experimental methods. Our work describes a quantitative analysis technique for modeling an adversary's knowledge about private information at workplace. Our technical enabler is a new emulation environment for conducting user experiments on attack behaviors. We develop a role-playing cyber game for our evaluation, where the participants take on the adversary role to launch ID theft attacks by answering challenge questions about a target. We measure an adversary's knowledge based on how well he or she answers the authentication questions about a target. We present our empirical modeling results based on the data collected from a total of 36 users.

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“…According to Smith et al [18], SNS users have no control over their published information on social network sites. In addition, users often do not know what SNS companies will do with their published data/messages.…”

Section: A Privacy Issues On Social Networking Sitesmentioning

confidence: 99%

Danger Theory-based Privacy Protection Model for Social Networks

Yohan

2014

Annals of Computer Science and Information Systems

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Abstract-Privacy protection issues in Social NetworkingSites (SNS) usually raise from insufficient user privacy control mechanisms offered by service providers, unauthorized usage of user's data by SNS, and lack of appropriate privacy protection schemes for user's data at the SNS servers. In this paper, we propose a privacy protection model based on danger theory concept to provide automatic detection and blocking of sensitive user information revealed in social communications. By utilizing the dynamic adaptability feature of danger theory, we show how a privacy protection model for SNS users can be built with system effectiveness and reasonable computing cost. A prototype based on the proposed model is constructed and evaluated. Our experiment results show that the proposed model achieves 88.9% detection and blocking rate in average for user-sensitive data revealed by the services of SNS.

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Big data privacy issues in public social media

Cited by 157 publications

References 10 publications

Integration of social media with healthcare big data for improved service delivery

Integration of social media with healthcare big data for improved service delivery

Role-Playing Game for Studying User Behaviors in Security: A Case Study on Email Secrecy

Danger Theory-based Privacy Protection Model for Social Networks

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