Distributing privacy policies over multimedia content across multiple online social networks

Patsakis, Constantinos; Zigomitros, Athanasios; Papageorgiou, Achilleas; Galván, Edgar

doi:10.1016/j.comnet.2014.08.023

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…This combination of roles and links can be assembled into a social network graph. If this PII and their inter-relationships are authentic, malicious attacks in cyberspace may likely evolve into real-life attacks, causing unimaginable harm [ 18,19 ], including Sybil attacks, tracking, profile cloning, defamation, identity theft, spamming, and phishing. Recently, researchers have focused on preventing these external attacks and proposed various prevention and rehabilitation methods.…”

Section: Related Workmentioning

confidence: 99%

Privacy measurement method using a graph structure on online social networks

Zhao

Tian

2021

ETRI Journal

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Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users ' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users ' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue.

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Section: Related Workmentioning

confidence: 99%

Privacy measurement method using a graph structure on online social networks

Zhao

Tian

2021

ETRI Journal

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“…If multiple OSNs are used, this mapping can be found especially quickly, accurately, and inexpensively due to SIL research. Therefore, the disclosure of PII may lead to malicious attacks from the cyberspace and real world [24,25]; examples of these attacks include, but are not limited to, tracking, defamation, spamming, phishing, identity theft, profile cloning, Sybil attack, etc.…”

Section: Related Workmentioning

confidence: 99%

“…However, they did not propose an effective method for solving this problem, only suggesting that users should disclose their PII as little as possible. Patsakis et al [25] proposed a framework based on scenarios with multiple social networks that can achieve the goal of protecting user privacy; however, it is impossible for each social network to interact with each other in practice. Erfan et al [38] combined attribute sensitivity and visibility, and used statistical fuzzy systems to solve the problem of privacy metrics on multiple OSNs.…”

Section: Related Workmentioning

confidence: 99%

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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“…Additionally, operators can use users' personal information arbitrarily for many purposes, such as viral marketing, targeted advertising, instant advertising, network promotion, and censorship. As users disclose more personal information [3,4], more malicious attacks may result in the real world and cyberspace [5,6,7,8], including but not limited to tracking, defamation, spam, phishing, identity theft, personal data cloning, and Sybil attacks [9].…”

Section: Introductionmentioning

confidence: 99%

Using User Behavior to Measure Privacy on Online Social Networks

Yang

Zhao

et al. 2020

IEEE Access

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Because social networks exemplify the phenomenon of homogeneity in complex networks, researchers generally believe that a user's privacy disclosure is closely related to that of the users around them, but we find that the related users studied in previous methods were not correct. That is, the analyzed user groups may have had nothing to do with the privacy disclosure of the target users. Since private information is time-sensitive, information held by users who are no longer in the same environment as the target user may no longer be true and have lost its value. For example, considering students and members of the working class, transfers to another school for further studies or job changes entail dramatic changes to most of their information. This lack of timeliness has an overarching impact on the effectiveness of social network analysis and privacy protection, but this problem has not been addressed by researchers. Therefore, we study and characterize this problem, add the user's behavior trace to solve this problem and measure the user's privacy status more accurately.

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Distributing privacy policies over multimedia content across multiple online social networks

Cited by 20 publications

References 22 publications

Privacy measurement method using a graph structure on online social networks

Privacy measurement method using a graph structure on online social networks

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

Using User Behavior to Measure Privacy on Online Social Networks

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