Journal ArticleDOI
Upper approximation based privacy preserving in online social networks
Saurabh Kumar,Pradeep Kumar +1 more
TLDR
The current study proposes to use upper approximation concept of rough sets for developing a solution for privacy preserving social network graph publishing that is capable of preserving the privacy of graph structure while simultaneously maintaining the utility or value that can be generated from the graph structure.Abstract:
With the advent of the online social network and advancement of technology, people get connected and interact on social network. To better understand the behavior of users on social network, we need to mine the interactions of users and their demographic data. Companies with less or no expertise in mining would need to share this data with the companies of expertise for mining purposes. The major challenge in sharing the social network data is maintaining the individual privacy on social network while retaining the implicit knowledge embedded in the social network. Thus, there is a need of anonymizing the social network data before sharing it to the third-party. The current study proposes to use upper approximation concept of rough sets for developing a solution for privacy preserving social network graph publishing. The proposed algorithm is capable of preserving the privacy of graph structure while simultaneously maintaining the utility or value that can be generated from the graph structure. The proposed algorithm is validated by showing its effectiveness on several graph mining tasks like clustering, classification, and PageRank computation. The set of experiments were conducted on four standard datasets, and the results of the study suggest that the proposed algorithm would maintain the both the privacy of individuals and the accuracy of the graph mining tasks.read more
Citations
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Journal ArticleDOI
Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey
Abdul Majeed,Sungchang Lee +1 more
TL;DR: In this article, the authors present a comprehensive survey of privacy preserving data publishing (PPDP) techniques for both graphs and relational data, and discuss the challenges of anonymizing both graphs, and elaborate promising research directions.
Journal ArticleDOI
Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks
TL;DR: Simulation results over four social network databases from Facebook, Google+, Twitter and YouTube demonstrate the efficiency of the proposed KFCFA algorithm to minimize the information loss of the published data and graph, while satisfying K-anonymity, L-diversity and T-closeness conditions.
Journal ArticleDOI
TSRAM: A time-saving k-degree anonymization method in social network
TL;DR: A time-saving k-degree anonymization method in social network (TSRAM) that anonymizes the social network graph without having to rescan the data set for different levels of anonymity and is effective to preserve the utility of the anonymized graph.
Journal ArticleDOI
Granular structure-based incremental updating for multi-label classification
Yuanjian Zhang,Duoqian Miao,Witold Pedrycz,Witold Pedrycz,Witold Pedrycz,Tianna Zhao,Jianfeng Xu,Jianfeng Xu,Ying Yu +8 more
TL;DR: The proposed granular structure system in bottom-up way provides a systematic view on label-specific based classification and an incremental three-way selective ensemble algorithm for multiple instances immigration is presented.
Journal ArticleDOI
Privacy-preserving human action recognition as a remote cloud service using RGB-D sensors and deep CNN
TL;DR: Experimental results show that the proposed approach is the best suitable candidate in “security-recognition accuracy (%)” trade-off relation among other image obfuscation as well as state-of-the-art schemes.
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