Network-aware privacy risk estimation in online social networks

Pensa, Ruggero G.; Blasi, Gianpiero Di; Bioglio, Livio

doi:10.1007/s13278-019-0558-x

Cited by 31 publications

(29 citation statements)

References 55 publications

(72 reference statements)

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“…Furthermore, research related to the negative impacts linked to the authenticity of social media and identities has increased in recent years [14]. This includes the analysis of the problems surrounding social media messages/posts regarding privacy, posts ending with unintended users, concerns on how to use social media platforms, who to follow and how people portray themselves in an inauthentic manner [14,15].…”

Section: Impact Of Fake News On Public Healthmentioning

confidence: 99%

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

Pulido

Ruiz-Eugenio

Redondo-Sama

et al. 2020

IJERPH

176

111

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One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.

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Section: Impact Of Fake News On Public Healthmentioning

confidence: 99%

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

Pulido

Ruiz-Eugenio

Redondo-Sama

et al. 2020

IJERPH

176

111

View full text Add to dashboard Cite

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“…In addition, attribute inference, link inference, identity links, and other research topics have been greatly developed, which means researchers must extend the privacy problem to the whole network environment. The more friends around a user who pay attention to his life, the greater the risk of privacy leakage he faces [15]. Pensa et al use the community discovery method to group users, measure the privacy in each group, and help users make reasonable changes to privacy settings through online learning.…”

Section: Related Workmentioning

confidence: 99%

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

Yang

Zhao

et al. 2020

Applied Sciences

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In recent years, privacy leakage events in large-scale social networks have become increasingly frequent. Traditional methods relying on operators have been unable to effectively curb this problem. Researchers must turn their attention to the privacy protection of users themselves. Privacy metrics are undoubtedly the most effective method. However, social networks have a substantial number of users and a complex network structure and feature set. Previous studies either considered a single aspect or measured multiple aspects separately and then artificially integrated them. The measurement procedures are complex and cannot effectively be integrated. To solve the above problems, we first propose using a deep neural network to measure the privacy status of social network users. Through a graph convolution network, we can easily and efficiently combine the user features and graph structure, determine the hidden relationships between these features, and obtain more accurate privacy scores. Given the restriction of the deep learning framework, which requires a large number of labelled samples, we incorporate a few-shot learning method, which greatly reduces the dependence on labelled data and human intervention. Our method is applicable to online social networks, such as Sina Weibo, Twitter, and Facebook, that can extract profile information, graph structure information of users' friends, and behavioural characteristics. The experiments show that our model can quickly and accurately obtain privacy scores in a whole network and eliminate traditional tedious numerical calculations and human intervention. the NetEase user database was leaked, and more than 100 million users' 163 and 126 mailbox information was leaked, including usernames, passwords, password security information, login IPs, and user birthdays. These privacy leakages may lead to a series of malicious acts [2-5], including but not limited to tracking, defamation, spam, phishing, identity theft, personal data cloning, Sybil attack, etc. However, academia has always focused on large-scale industrial networks, such as the Internet of Things, smart grids, and cloud storage networks, ignoring the privacy protection of individual users because the privacy leakage of large-scale networks causes substantial visible economic losses, but the impact of personal information leakage is scattered and small. Moreover, users' use of social networks is a process of sharing public information. Applying privacy protection methods to traditional industrial networks will damage user experience.The existing privacy protection methods in social networks include anonymity, decentralisation, encryption, information security regulations, fine-grained privacy settings and access control, and improving user privacy awareness and privacy behaviour [6]. The first four methods need to rely on operators, which has proven to be unreliable. With the rapid development of attribute inference, link inference, personal identification, and community discovery, the role of anonymisation is becom...

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“…Thus, many different metrics and methods have been proposed with the goal of assessing the risk of privacy leakage in posting activities [1,23]. Most research efforts, however, focus on measuring the overall exposure of users according to their privacy settings [8,19] or position within the network [18].…”

Section: Related Workmentioning

confidence: 99%

“…The problem of private content analysis has already been investigated as a way to characterize anonymous vs. non anonymous content posting in specific social media [5,15,16] or question-and-answer platforms [14]. However, the link between anonymity and sensitive contents is not that obvious: users may post anonymously because, for instance, they are referring to illegal matters (e.g., software/steaming piracy, black market and so on); conversely, fully identifiable persons may post very sensitive contents simply because they are underestimating the visibility of their action [18,19]. Although CSA has some points in common with anonymous content analysis and the well-known sentiment analysis task, we show that it has its own peculiarities and may lead to a brand new branch of research, opening many intriguing challenges in several computer science and linguistics fields.…”

Section: Introductionmentioning

confidence: 99%

Towards Content Sensitivity Analysis

Battaglia

Bioglio

Pensa

2020

Lecture Notes in Computer Science

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With the availability of user-generated content in the Web, malicious users dispose of huge repositories of private (and often sensitive) information regarding a large part of the world's population. The self-disclosure of personal information, in the form of text, pictures and videos, exposes the authors of such contents (and not only them) to many criminal acts such as identity thefts, stalking, burglary, frauds, and so on. In this paper, we propose a way to evaluate the harmfulness of any form of content by defining a new data mining task called content sensitivity analysis. According to our definition, a score can be assigned to any object (text, picture, video...) according to its degree of sensitivity. Even though the task is similar to sentiment analysis, we show that it has its own peculiarities and may lead to a new branch of research. Thanks to some preliminary experiments, we show that content sensitivity analysis can not be addressed as a simple binary classification task.

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Network-aware privacy risk estimation in online social networks

Cited by 31 publications

References 55 publications

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

Towards Content Sensitivity Analysis

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