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

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 privacy-preserving 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

A Hybrid Probabilistic Privacy Preserving Based Community Detection Model on Online Social Networking Data

Privacy preserving plays a vital role on the online social networking sites due to high dimensionality and data size. Community detection is used to find the social relationships among the node edges and links. However, most of the conventional models are difficult to process the community structure detection due to high computational time and memory. Also, these models require contextual weighted nodes information for privacy preserving process. In order to overcome these issues, an advanced probabilistic weighted based community detection and privacy preserving framework is developed on the large social networking data. In this model, a filter based probabilistic model is developed to remove the sparse values and to find the weighted community detection nodes and its profiles for privacy preserving process. Experimental results show that the filter based probabilistic community detection framework has better efficiency in terms of normalized mutual information, Q, rand index  and runtime (ms)