A privacy self-assessment framework for online social networks

Pensa, Ruggero G.; Blasi, Gianpiero Di

doi:10.1016/j.eswa.2017.05.054

Cited by 36 publications

(28 citation statements)

References 32 publications

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“…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. Zeng et al believe that a single user's privacy disclosure depends on his trust in the friends around him and proposes a framework based on trust awareness to evaluate a user's privacy leakage [16]. Alsakal et al [17] introduced information metrics for users in a whole social network by using information entropy theory and discussed the impact of individual identifying information and combinations of different pieces of information on user information disclosure.…”

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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Section: Related Workmentioning

confidence: 99%

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

Yang

Zhao

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

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“…Similarly, Li Minghui et al [36] believed that attackers could use the background knowledge of public neighbours to obtain the privacy of victims; they used K-anonymity and L-diversity to approach this challenge, but these two approaches do not completely solve the issue. Ruggero G. Pensa et al [23] used a circle-based definition of privacy score to measure privacy leakage and applied a learning approach to help users change privacy settings.…”

Section: Related Workmentioning

confidence: 99%

“…Given this trend, malicious attackers can integrate a complete online role via profiles across multiple OSNs, and serious harm can be caused to the real individual in various ways [21]. Given this background, privacy protection on a single platform is far more than enough to manage [8][9][10][11]22,23].At present, there is no perfect solution to this problem because users have varied requirements for privacy protection that depend on the context. Therefore, the best solution is to provide a method to quantify the privacy of individuals, transform the virtual concept of privacy into a visible physical space, help users accurately recognize the state of their privacy and help users improve their privacy.…”

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confidence: 99%

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A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage

Yang

Chen

et al. 2018

Applied Sciences

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Recently, the number of people who are members of multiple online social networks simultaneously has increased. However, if these people share everything with others, they risk their privacy. Users may be unaware of the privacy risks involved with sharing their sensitive information on a network. Currently, there are many research efforts focused on social identity linkage (SIL) on multiple online social networks for commercial services, which exacerbates privacy issues. Many existing studies consider methods of encrypting or deleting sensitive information without considering if this is unreasonable for social networks. Meanwhile, these studies ignore privacy awareness, which is rudimentary and critical. To enhance privacy awareness, we discuss a user privacy exposure measure for users who are members of multiple online social networks. With this measure, users can be aware of the state of their privacy and their position on a privacy measurement scale. Additionally, we propose a straightforward method through our framework to reduce information loss and foster user privacy awareness by using spurious content for required fields.Facebook users' information, in order to support Donald Trump's election campaign. Facebook knew of this in the past two years, but no measures were taken; in 2017, Twitter publicly announced that it is abandoning the DNT(Do not track) privacy protection standard; according to a recent study, more than 6.05 billion pieces of personal information have been disclosed in China [4]. Thus, privacy protection ultimately depends on the users.In the field of computer security, the basic principle of protecting privacy is preventing information from escaping its intended boundaries. However, privacy on OSNs is contrary to the goal of people using them. The only way to mitigate this paradox is to find a reasonable boundary between protecting privacy and disclosing PII. However, although people find it inherently easy to understand physical concepts, they have difficulty with virtual concepts, and privacy is a virtual concept. Initially, most OSNs offer their users privacy control, which is simple to use but limited; for example, a privacy control may enable users to set their entire profile as public, visible to friends only, or private (visible only to the user). With growing demand from users and increasing attention to privacy in the media, many OSNs (e.g., Facebook) have started offering their users more control, such as the ability to set the visibility of individual items. However, if interfaces become overly complicated, then users will not understand the settings or find them too cumbersome, and thus, they might set them in an unreasonable manner or ignore them. In a case study, Gross and Acquisti [5] show that most users do not change the default privacy settings provided by the OSN when sharing a large amount of information on their profile. In another case study, Tufecki [6] concludes that privacy-aware users are more reluctant to join social networks, but once they join, they still disc...

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“…Social networks have helped organizations adapt their content of advertising to the needs of individual customers and personalise advertisements and have also enabled the customer to manage the qualitative and quantitative parameters of advertising (content, length, time, place, etc.) to some extent [3,6,26,[33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Advertising Campaigns on Social Media Networks

et al. 2018

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Abstract:As the virtual environment is constantly changing, not only users' informational and knowledge needs but also the means and channels of communication with customers applied by organizations change. There is a noticeable trend to move more and more advertising campaigns to social media networks because of the opportunities they provide to organizations and users, which results in the ever-increasing popularity of social media networks and a number of their users. Such a transition is explained by one of the main objectives organizations have: to inform their customers in an appropriate way and receive feedback on social media networks, which is difficult when traditional advertising channels and means are applied. Since advertising campaigns on social media networks are evolving rapidly, their assessment factors and methods, which receive controversial opinions in both scientific literature and practice, change too. Researchers assess and interpret the factors that influence the effectiveness of advertising campaigns on social media networks differently. Thus, a problem arises: how should we evaluate which approach is more capable of accurately and fully reflecting and conveying reality? In this research, this problem is studied by connecting approaches of different researchers. These approaches are linked to the effectiveness assessment of advertising campaigns on social media network aspects. To achieve the objective of this study, such research methods as analysis of scientific literature, multiple criteria and expert assessment (a structured survey and an interview) were applied. During the study, out of 39 primary assessment factors, eight primary factors that influence the effectiveness of advertising campaigns on social media networks were identified: sales, content reach, traffic to website, impressions, frequency, relevance score, leads and audience growth.

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A privacy self-assessment framework for online social networks

Cited by 36 publications

References 32 publications

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage

Evaluation of Advertising Campaigns on Social Media Networks

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