Privacy protection issues in social networking sites

Ho, Ai; Maïga, Abdou; Aïmeur, Esma

doi:10.1109/aiccsa.2009.5069336

Cited by 47 publications

(34 citation statements)

References 7 publications

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“…In [7], two different approaches for categorizing data are mentioned. The first one is based on a survey in which users of OSNs were asked which data they would place on their profile.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the resulting classification only considers the items that were mentioned by the participants of the survey. In the second approach presented in [7], user data is divided by focusing on the data's impact on privacy. While reasonable for categorizing privacy settings it is unsuitable for developing a general-purpose taxonomy as many other dimensions would be omitted.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous studies have been conducted on privacy issues on OSNs in general [7], [8] as well as on people's awareness in this context [9], [10] and on potential hazards [11], [12]. Proposals for improving the user's understanding of disclosed information [13] and improving privacy protection on OSNs [14] have been made.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In [7], two different approaches for categorizing data are mentioned. The first one is based on a survey in which users of OSNs were asked which data they would place on their profile.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Generally Social Networking Sites (SNS) users depend on the SNS platform for managing their social identities. Although SNS providers have introduced privacy settings to protect users against threats, these are insufficient and the access control models are difficult to be understood by novice users [1]. Unexpectedly, even those who are aware of the security and privacy threats and the preventive tools that combat those threats, lack the motivation to utilize security features to protect themselves.…”

Section: Introductionmentioning

confidence: 99%

Safe sonet: a framework for building trustworthy relationships

Neethu¹,

Harini²

2018

IJET

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The advent of easy to use services and the ability to bridge boundaries in space and time has increasingly changed our social lives and com-munication habits. Being unaware of their audience people on social networks inconsiderately share many personal items. Generally Social Networking Sites (SNS) users depend on the SNS platform for managing their social identities. Although SNS providers have introduced privacy settings to protect users against threats, these are insufficient and the access control models are difficult to be understood by novice users [1]. Unexpectedly, even those who are aware of the security and privacy threats and the preventive tools that combat those threats, lack the motivation to utilize security features to protect themselves. The paper discusses a frame-work (Safe SoNet) that aims to provide a plat-form for secure sharing of posts. The objective of this framework is twofold. Firstly, to analyze user behavior by retrieving user information from various social networking sites, addressing transliteration issues. Secondly, apply the user behavior to quantify the trust score of a connect, which would in turn form a decision making parameter to decide on the peer with whom the information can be securely shared.

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“…According to Ho et al [1], SNS is a website that provides a virtual community for people with similar interests in particular subject, or just to "hangout" together. Based on the definition of SNS, people can use SNS services to share information, thoughts and feelings, chat with other users, play online games with other users, and even promote their own businesses to other users [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

Danger Theory-based Privacy Protection Model for Social Networks

Yohan

2014

Annals of Computer Science and Information Systems

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Abstract-Privacy protection issues in Social NetworkingSites (SNS) usually raise from insufficient user privacy control mechanisms offered by service providers, unauthorized usage of user's data by SNS, and lack of appropriate privacy protection schemes for user's data at the SNS servers. In this paper, we propose a privacy protection model based on danger theory concept to provide automatic detection and blocking of sensitive user information revealed in social communications. By utilizing the dynamic adaptability feature of danger theory, we show how a privacy protection model for SNS users can be built with system effectiveness and reasonable computing cost. A prototype based on the proposed model is constructed and evaluated. Our experiment results show that the proposed model achieves 88.9% detection and blocking rate in average for user-sensitive data revealed by the services of SNS.

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Privacy protection issues in social networking sites

Cited by 47 publications

References 7 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

Safe sonet: a framework for building trustworthy relationships

Danger Theory-based Privacy Protection Model for Social Networks

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