Your Privacy Is Your Friend's Privacy: Examining Interdependent Information Disclosure on Online Social Networks

Alsarkal, Yaqoub; Zhang, Nan; Xu, Heng

doi:10.24251/hicss.2018.111

Cited by 18 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of Twitter, while users can protect their own tweets through the privacy setting, they cannot prevent others from mentioning them (i.e., @UserA) in a public tweet and "peerdisclosing" certain private information about them. While the intuition behind this "privacy peerdisclosure" is straightforward -and empirical evidence of its existence on Twitter has been reported [4] for non-protected Twitter users -this paper reports the first evidence showing that, even after a Twitter user elects to protect all tweets, substantial private information, including photos, protected tweets, and sensitive information such as date of birth and home address, are still continuously disclosed to the public through peerdisclosure. Such peer-disclosure takes place in a variety of forms, e.g., the mentioning of a user ("@") in a tweet, a well-known "off-the-book" trick ("dotstart") of replying publicly to a protected tweet that is otherwise prohibited (by the Twitter system design) from being retweeted or quoted, etc.…”

Section: Introductionmentioning

confidence: 89%

“…Recall from Section 2 recent research that chartered the conceptual framework for privacy peer-disclosure in OSNs [5,11,25] and verified its existence in realworld OSNs like Twitter [4]. What is missing in the literature, however, is a study of the interplay between peer-disclosure and a user's privacy settings.…”

Section: Research Questionsmentioning

confidence: 99%

“…Recent work [4] identified two main types of activities that lead to the peer-disclosure of user A's private information by user B: 1) the proactive mentioning of A in B's OSN activity, e.g., the tagging of A in a tweet or a photo posted by B; and 2) B's reactive interactions to A's activities, e.g., a reply posted by B to A's tweet, or B's comment on A's Facebook post.…”

Section: Peer-disclosure Activitiesmentioning

confidence: 99%

See 2 more Smart Citations

Protecting Privacy on Social Media: Is Consumer Privacy Self-Management Sufficient?

Alsarkal

Zhang

2018

SSRN Journal

Self Cite

View full text Add to dashboard Cite

Among the existing solutions for protecting privacy on social media, a popular doctrine is privacy selfmanagement, which asks users to directly control the sharing of their information through privacy settings. While most existing research focuses on whether a user makes informed and rational decisions on privacy settings, we address a novel yet important question of whether these settings are indeed effective in practice. Specifically, we conduct an observational study on the effect of the most prominent privacy setting on Twitter, the protected mode. Our results show that, even after setting an account to protected, real-world account owners still have private information continuously disclosed, mostly through tweets posted by the owner's connections. This illustrates a fundamental limit of privacy self-management: its inability to control the peer-disclosure of privacy by an individual's friends. Our results also point to a potential remedy: A comparative study before vs after an account became protected shows a substantial decrease of peerdisclosure in posts where the other users proactively mention the protected user, but no significant change when the other users are reacting to the protected user's posts. In addition, peer-disclosure through explicit specification, such as the direct mentioning of a user's location, decreases sharply, but no significant change occurs for implicit inference, such as the disclosure of birthday through the date of a "happy birthday" message. The design implication here is that online social networks should provide support alerting users of potential peer-disclosure through implicit inference, especially when a user is reacting to the activities of a user in the protected mode.

show abstract

Section: Introductionmentioning

confidence: 89%

Section: Research Questionsmentioning

confidence: 99%

Section: Peer-disclosure Activitiesmentioning

confidence: 99%

See 1 more Smart Citation

Protecting Privacy on Social Media: Is Consumer Privacy Self-Management Sufficient?

Alsarkal

Zhang

2018

SSRN Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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...

show abstract

“…While users share information on social networks with friends and even with potential friends in a safe manner, there is a need to present a model to provide users their information secrecy and privacy. Information disclosure may lead to privacy risks for the user's friends as well [18]. Sharing personally identifiable information (PII) (typically, an identifying variable is one that defines an attribute of an individual that is visible and evident, is recorded [such as social security number, employee ID, patient ID, etc.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks

2018

View full text Add to dashboard Cite

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner.

show abstract

Your Privacy Is Your Friend's Privacy: Examining Interdependent Information Disclosure on Online Social Networks

Cited by 18 publications

References 35 publications

Protecting Privacy on Social Media: Is Consumer Privacy Self-Management Sufficient?

Protecting Privacy on Social Media: Is Consumer Privacy Self-Management Sufficient?

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks

Contact Info

Product

Resources

About