Measuring Privacy in Online Social Networks

Ananthula, Swathi; Abuzaghleh, Omar; Alla, Navya Bharathi; Chaganti, Swetha Prabha; kaja, Pragna chowdary; Mogilineedi, Deepthi

doi:10.5121/ijsptm.2015.4201

Cited by 14 publications

(11 citation statements)

References 8 publications

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“…A third-party from class B can request for all three types of attributes of ID, QI, and S as there is no elimination of attributes at this level. The anonymization algorithm calculates the percentage of privacy leakage of a request ( ( ) ) and compares the result with the percentage of privacy leakage in level 1( ( ) ) [48]. If it is less than the percentage of privacy leakage in level 1, the anonymization algorithm will grant access to the third-party to level 1.…”

Section: Classification Algorithmmentioning

confidence: 99%

A privacy-preserving model to control social interaction behaviors in social network sites

Kavianpour

Tamimi

Shanmugam

2019

Journal of Information Security and Applications

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Social Network Sites (SNSs) served as an invaluable platform to transfer information across a large number of users. SNSs also disseminate users data to third-parties to provide more interesting services for users as well as gaining profits. Users grant access to third-parties to use their services, although they do not necessarily protect users' data privacy. Controlling social network data diffusion among users and third-parties is difficult due to the vast amount of data. Hence, undesirable users' data diffusion to unauthorized parties in SNSs may endanger users' privacy. This paper highlights the privacy breaches on SNSs and emphasizes the most significant privacy issues to users. The goals of this paper are to i) propose a privacypreserving model for social interactions among users and third-parties; ii) enhance users' privacy by providing access to the data for appropriate third-parties. These advocate to not compromising the advantages of SNSs information sharing functionalities.

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Section: Classification Algorithmmentioning

confidence: 99%

A privacy-preserving model to control social interaction behaviors in social network sites

Kavianpour

Tamimi

Shanmugam

2019

Journal of Information Security and Applications

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“…An individual's identity could be derived from data generated from both online and offline worlds (Creese et al 2013;Ananthula et al 2015;Li and Wang 2015). In particular, one's offline identity includes data elements such as name, date of birth, gender, nationality, and relationships; online identity includes data elements such as username, browsing history, and email.…”

Section: Re-identification Risksmentioning

confidence: 99%

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

Alsarkal¹,

Zhang²,

Xu³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users' private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user's private information by him/herself; and co-disclosure, i.e., the disclosure of the user's private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide a framework for understanding the commonalities and distinctions between self-vs. co-disclosure, especially pertaining to different types of private information. To address this gap, this paper presents a data-driven study that builds upon an innovative measurement for quantifying the extent to which others' co-disclosure could lead to actual privacy harm. The results demonstrate the significant harm caused by co-disclosure and illustrate the differences between the identity elements revealed through self-and co-disclosure.

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“…Albeit users may consider whether there is any possibility of disclosing identifying information when they are going to publish a post, as long as the personal information exposed in the public space, there is the possibility of being 9 compromised. The problem of privacy security boils down to the privacy risk caused by the internet users' active sharing (Ananthula, Abuzaghleh, Alla, Chaganti, Kaja, & Mogilineedi, 2015). The ability to manage and control the personal information can effectively reduce the probability of privacy leakage.…”

Section: Privacy Preservation Measuresmentioning

confidence: 99%

Comparative Evaluations of Privacy on Digital Images

Zhang

Yan

2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Privacy preservation on social media is a societal issue nowadays. In recent years, with the continuous occurrence of the privacy leaks of user information and file, privacy and security issues have received unprecedented attention. Albeit a slew of mechanisms one available in protecting sensitive individual data, there are inadequate solutions to the critical concerns on privacy violations. Furthermore, the approaches of evaluating the potential privacy risks on social networking activities have not been yet paid enough attention. In order to preserve privacy effectively, the content is released safely on social media. This thesis introduces the necessity of protecting the image privacy and effective protection methods. The problems to be investigated that need to be solved urgently are put forward. The key factors affecting privacy are probed in depth. Also, the computer vision technology plays an essential role in the image privacy. Moreover, the theory of differential privacy is adopted, which can protect the image analysis data for broader research and cooperation. We combine qualitative method with AHP (Analytic Hierarchy Process) model to provide a more reasonable measure of privacy weights. Resultant analysis of the survey also provides metrics for evaluating privacy accuracy. The experimental results demonstrate that the model of image privacy evaluation proposed in this thesis can effectively and accurately measure the level of image privacy. Thus, the degree of picture privacy can be intuitively measured and the privacy can be adequately protected.

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Measuring Privacy in Online Social Networks

Abstract: ABSTRACT

Cited by 14 publications

References 8 publications

A privacy-preserving model to control social interaction behaviors in social network sites

A privacy-preserving model to control social interaction behaviors in social network sites

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

Comparative Evaluations of Privacy on Digital Images

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