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Popular
Date
Feb 7, 2018
Publishing social network data for research purposes has raised serious concerns for individual privacy. There exist many privacy-preserving works that can deal with different attack models. In this paper, we introduce a novel privacy attack model and refer it as a mutual friend attack. . The area of privacy inside a social network encompasses a large breadth, based on how privacy is defined. In [1], authors consider an attack against an anonymized network. In their model, the network consists of only nodes and edges.
Popular
Date
Feb 19, 2018
Generally, a social network is a social structure made up of individuals or organizations, which are connected, by one or more specific types of interdependency, such as friendship, common interest, and exchange of finance, …. For a published social network G the above approaches aim to prevent attacks based on vertex of G, we define a friendship attack as follows. degrees, the objective of this paper is to prevent friendship Definition 1.
Popular
Date
Feb 2, 2018
an attacks a social network to learn users’ private sensitive information. Studies on the challenges of protecting the privacy of individuals in social networks have find out only in the few years, and they have focus on. Preventing Private Information Inference Attacks on Social Networks Technical Report UTDCS-03-09 The area of privacy inside a social network encompasses a large breadth, based on how privacy is defined. In [1], authors consider an attack against an we choose to evaluate the effects of privacy on removing friendship links instead of
Popular
Date
Feb 9, 2018
Index Terms—Social network analysis, data mining, social network privacy 1 INTRODUCTION S OCIAL networks are online applications that allow their defined in the social network. For any friendship link 1 i. , between user i i and user i , PREVENTING PRIVATE INFORMATION INFERENCE ATTACKS ON SOCIAL …. 2) Stronger privacy guarantee: we added an efficient algorithm to make the graph satisfy a strong privacy guarantee, differential privacy, and thus allow the app server to release the underlying graph of the entire social network without jeopardizing users’ privacy.
