Enabling Privacy as a Fundamental Construct for Social Networks

Maximilien, E. Michael; Grandison, Tyrone; Liu, Kun; Sun, Tony; Richardson, Dwayne; Guo, Sherry

doi:10.1109/cse.2009.431

Cited by 14 publications

(10 citation statements)

References 2 publications

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“…A previous work proposed by Liu and Terzi (Liu and Terzi, 2010;Maximilien et al, 2009) developed a 2-dimensional matrix to compute the privacy risk, considering the sensitivity and the visibility of a user's personal data to the users of an OSN. Our future work aims to extend this model also to Apps and AppPs.…”

Section: Discussionmentioning

confidence: 99%

“…To estimate the privacy risk for a user, Maximilien et al (Maximilien et al, 2009) initially proposed a Privacy-as-a-Service formula. This formula is used to compute the privacy risk as the product of sensitivity and visibility of personal data.…”

Section: Related Workmentioning

confidence: 99%

“…This formula is used to compute the privacy risk as the product of sensitivity and visibility of personal data. Liu and Terzi (Liu and Terzi, 2010) extended this work (Maximilien et al, 2009) and proposed a framework for computing the privacy risk using a probabilistic model based on the Item Response Theory (IRT). Although, IRT presents interesting results to compute the sensitivity of the user's personal data, there is a lack of evaluation for the visibility.…”

Section: Related Workmentioning

confidence: 99%

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Collateral Damage of Online Social Network Applications

Symeonidis

Tsormpatzoudi

Preneel

2016

Proceedings of the 2nd International Conference on Information Systems Security and Privacy

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Abstract:Third party application providers in Online Social Networks can collect personal data of users through their friends without the user's awareness. In some cases, one or more application providers may own several applications and thus the same provider may collect an excessive amount of personal data, which creates a serious privacy risk. Previous research has developed methods to quantify privacy risks in Online Social Networks. However, most of the existing work does not focus on the issues of personal data disclosure via the user's friends applications and application providers. The aim of this paper is to investigate the need for solutions that can compute privacy risk related to applications and application providers. In this work we perform a legal and technical analysis of the privacy threats stemming from the collection of personal data by third parties when applications are installed by the user's friends. Particularly, we examine the case of Facebook as it is the most popular Online Social Network nowadays.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Collateral Damage of Online Social Network Applications

Symeonidis

Tsormpatzoudi

Preneel

2016

Proceedings of the 2nd International Conference on Information Systems Security and Privacy

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“…Maximilien et al [42] proposed a privacy score model to compute the risk of users who participate in online social networks considering the dichotomous approach. For creating the model, they considered visibility and sensitivity of users' information.…”

Section: Privacy Risk Scorementioning

confidence: 99%

“…From the privacy score equation, it can be seen that there is a need to compute the visibility and sensitivity for the final measurement of users' privacy score. Hence, Maximilien et al [42] provided formulas for sensitivity and visibility:…”

Section: Privacy Risk Scorementioning

confidence: 99%

User's privacy in recommendation systems applying online social network data: a survey and taxonomy

Aghasian

Garg

Montgomery

2019

Big Data Recommender Systems - Volume 1: Algorithms, Architectures, Big Data, Security and Trust

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Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.

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Security, Privacy, and Trust in Social Networks

Karuppanan¹

2012

Computational Social Networks

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Enabling Privacy as a Fundamental Construct for Social Networks

Abstract: The current set of social networking platforms, e.g. Facebook

Cited by 14 publications

References 2 publications

Collateral Damage of Online Social Network Applications

Collateral Damage of Online Social Network Applications

User's privacy in recommendation systems applying online social network data: a survey and taxonomy

Security, Privacy, and Trust in Social Networks

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