Background And Motivation On Online Social Networks

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Background and Motivation:
Another form of attack on online social networks is the Social Link Disclosure based attacks, in which the goal of an attacker is to obtain knowledge of a significant fraction of the links in the network. The value of participating in an online social network for a user lies majorly in the ability to leverage the structure of the social network graph. But, knowledge of this social graph by parties other than the service provider paves the way for powerful data mining, some of which may not be desirable to the users. The ability to conduct these analyses on social networks is becoming increasingly important in the data mining, database and theoretical computer science communities [4.32]. Since nodes in social
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To analyse the relationship between the lookahead provided by the network provider and the number of user accounts required to subvert a portion of the network. Analyze the usage of weighted graphs to define and/or augment a link structure, that makes the structure more robust and resilient to attacks. To compare the two solutions and conclude on the pros and cons of both.
Social Link Disclosure-based Attack: Links refer to the connections between vertices in a network. In the context of online social networks, links are the connections between users of the network. But not all information related to these connections are suitable to be disclosed to the public.
Goal of the Attack: Social link disclosure-based attacks aim at disclosing such sensitive information related to the links of the social network, and thereby discover the link structure of the network. Although online social networks are a new domain with a not-so-large research information base , knowledge regarding the structure of the network enables the attacker to analyze the network using the powerful graph mining algorithms that have existed for centuries and have an extremely rich research information base.
There are several approaches to deal with this problem. In this survey, we consider two approaches to Privacy-Preserving Link Analysis. One, by limiting the lookahead of the interface of an online social network, as suggested by [ ]. Two, by
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