Privacy in Online Social Networks

Beye, Michael; Jeckmans, Arjan; Erkin, Zekeriya; Hartel, Pieter H.; Lagendijk, Reginald L.; Tang, Qiang

doi:10.1007/978-1-4471-4051-1_4

Cited by 43 publications

(73 citation statements)

References 30 publications

(24 reference statements)

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“…In an initial step and based on Section II, a thorough literature analysis reveals in essence the following three related approaches: Schneier [19], its refinement by Arnes et al [25] and the classification by Beye et al [24]. Table I correlates the data elements of these approaches and the taxonomy proposed in this work, while the subsequent discussion of this section highlights conceptual similarities and deviations.…”

Section: Proposed Taxonomymentioning

confidence: 94%

“…Beye et al [24] follow a different approach that builds upon the definition of OSN by boyd [sic] and Ellison [1] from which three data types are deduced. Additional six data types are derived by focusing on the goals of different OSNs.…”

Section: Related Workmentioning

confidence: 99%

“…Additional six data types are derived by focusing on the goals of different OSNs. Compared to the approaches discussed so far, the one in [24] contains a well-founded explanation on the origin of the data types. However, their definition (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…The analysis of Table I leads to several observations: Firstly, it reveals that to some extent terminology is not consistently used, such as the different understanding of behavioral data [19], [25] and behavioral information [24]. Secondly, a general lack of granularity can be attributed to some existing data type definitions, as observable in the generic conceptualization of profiles in [24].…”

Section: Proposed Taxonomymentioning

confidence: 99%

See 3 more Smart Citations

Taxonomy for Social Network Data Types from the Viewpoint of Privacy and User Control

Richthammer

Netter

Riesner

et al. 2013

2013 International Conference on Availability, Reliability and Security

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Abstract-The growing relevance and usage intensity of Online Social Networks (OSNs) along with the accumulation of a large amount of user data has led to privacy concerns among researchers and end users. Despite a large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing, hence leading to confusion in related discussions. This paper proposes a taxonomy for data types on OSNs based on a thorough literature analysis and a conceptualization of typical OSN user activities. It aims at clarifying discussions among researchers, benefiting comparisons of data types within and across OSNs and at educating the end user about characteristics and implications of OSN data types. The taxonomy is evaluated by applying it to four major OSNs.

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Section: Proposed Taxonomymentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Proposed Taxonomymentioning

confidence: 99%

See 2 more Smart Citations

Taxonomy for Social Network Data Types from the Viewpoint of Privacy and User Control

Richthammer

Netter

Riesner

et al. 2013

2013 International Conference on Availability, Reliability and Security

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“…As surveyed in [1], a large number of OSNs exist, among which Facebook, MySpace, Google +, and Twitter are the most popular ones.…”

Section: Introductionmentioning

confidence: 99%

Towards a Privacy-Preserving Solution for OSNs

Tang

2013

Network and System Security

Self Cite

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Abstract. In this short paper, we describe a solution to protect users' privacy in online social networks (OSNs). The solution achieves the following functionalities: (1) it enables users to store their private data securely; (2) it enables users, from the same or different OSNs, to compute their similarity through a secure protocol; (3) it enables similar users to establish a session key for secure communication. Different from existing solutions in the literature, which often rely on a global public key infrastructure or/and traditional key distribution techniques, the proposed solution leverages on the trust between friends and the entropy of users' private attributes.

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A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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Privacy in Online Social Networks

Cited by 43 publications

References 30 publications

Taxonomy for Social Network Data Types from the Viewpoint of Privacy and User Control

Taxonomy for Social Network Data Types from the Viewpoint of Privacy and User Control

Towards a Privacy-Preserving Solution for OSNs

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

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