Detecting and predicting privacy violations in online social networks

Kafalı, Özgür; Günay, Akın; Yolum, Pınar

doi:10.1007/s10619-013-7124-8

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…Kafali et al develop PROTOSS [16], where the users' privacy agreements are checked against an OSN using model checking. The number of states that are generated even in a small network is huge and may not be applicable in large networks.…”

Section: Related Workmentioning

confidence: 99%

PriGuard: A Semantic Approach to Detect Privacy Violations in Online Social Networks

Kökciyan

Yolum

2016

IEEE Trans. Knowl. Data Eng.

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Social network users expect the social networks that they use to preserve their privacy. Traditionally, privacy breaches have been understood as malfunctioning of a given system. However, in online social networks, privacy breaches are not necessarily a malfunctioning of a system but a byproduct of its workings. The users are allowed to create and share content about themselves and others. When multiple entities start distributing content without a control, information can reach unintended individuals and inference can reveal more information about the user. Accordingly, this paper first categorizes the privacy violations that take place in online social networks. Our categorization yields that the privacy violations in online social networks stem from intricate interactions and detecting these violations requires semantic understanding of events. Our proposed approach is based on agent-based representation of a social network, where the agents manage users' privacy requirements by creating commitments with the system. The privacy context, including the relations among users or content types are captured using description logic. The proposed detection algorithm performs reasoning using the description logic and commitments on a varying depths of social networks. We implement the proposed model and evaluate our approach using real-life social networks.

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Section: Related Workmentioning

confidence: 99%

PriGuard: A Semantic Approach to Detect Privacy Violations in Online Social Networks

Kökciyan

Yolum

2016

IEEE Trans. Knowl. Data Eng.

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“…this also generates a sensitive area of various privacy related issues while exchanging such type of information. O. Kafali [12] et. al.…”

Section: Fuzzy C-meansmentioning

confidence: 99%

Enhanced Techniques for Filtering of Wall Messages over Online Social Networks (OSN) User Profiles

Choudhary¹,

Yadav²,

Jain³

2015

IJWMT

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Online Social Networks enables various users to connect and share their messages publicly and privately. On one hand it provides advantages to the users to connect and share but on the other hand it provides disadvantage of being attacks or post messages which contains negative or abuse words. Hence OSN provides various filtering rules for security against these wall messages. Although there are various filtering rules and classifiers implemented for the filtering of these users wall messages in popular OSN such as Twitter and Facebook. But in the proposed methodology not only filtering of these wall messages is done but the categorization of normal or negative messages are identified and hence on the basis users can be blacklisted. The proposed methodology is compared with FCM and SVM for clustering and classification of messages. This approach efficiently categorizes the messages but restricts for generating filtering rules and blacklist management. Thus the approach with FCM and J48 first initializes clustering using FCM followed by generation of rules using J48 based decision tree. Hence on the basis of the rules generated message are classified and message which doesn"t contain attacks is then filtered on the basis of dictionary which contains a list of abuse words. The methodology is implemented by applying FCM and SVM and a comparison is done with FCM and J48 for the performance on the basis of accuracy to detect abnormal messages.

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“…For example, sharing products preferences and reviews, occasion's announcement and invitations, travel destinations, is commonplace. Statistics also show that 55% of the OSNs users have shared publicly their personal information, photos, and even their physical descriptions . Figure shows different types of personal information posted on OSNs …”

Section: Introductionmentioning

confidence: 99%

An efficient approach of improving privacy and security in online social networks

Almasoud

Almogren

Hassan

et al. 2017

Concurrency and Computation

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Online social networks (OSNs) have become popular and widely used by many people nowadays.As those OSNs increase, the virtual social interactions have resulted in increasing privacy and security concern. Many approaches have been proposed in order to provide users with "better" privacy protection, but none of those approaches have "fully succeeded," due to their conflict with service providers 0 economic benefits or due to the suggestion of changing the OSNs architecture. In this paper, we propose a new model that would improve privacy and security in OSNs by preventing users from revealing private and or confidential information online as possible. We endeavor to create a balance between privacy and security improvement versus the service provider business model.

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Detecting and predicting privacy violations in online social networks

Cited by 17 publications

References 17 publications

PriGuard: A Semantic Approach to Detect Privacy Violations in Online Social Networks

PriGuard: A Semantic Approach to Detect Privacy Violations in Online Social Networks

Enhanced Techniques for Filtering of Wall Messages over Online Social Networks (OSN) User Profiles

An efficient approach of improving privacy and security in online social networks

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