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1
Privacy Protection Method for Sensitive Weighted Edges in Social Networks

( Weihua Gong ),( Rong Jin ),( Yanjun Li ),( Lianghuai Yang ),( Jianping Mei ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2
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  ScienceON
  
  KISS
Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.
2
Energy-Efficient Traffic Splitting for Time-Varying Multi-RAT Wireless Networks

Wu, Weihua,Yang, Qinghai,Gong, Peng,Kwak, Kyung Sup IEEE 2017 IEEE Transactions on Vehicular Technology VT Vol.66 No.7
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<P>This paper investigates the energy-efficient traffic splitting for time-varying wireless networks, which have been configured with multiple radio access technologies (multi-RATs). A single stream of the media content is split into multiple segments, which could be transmitted over multiple RATs simultaneously so that the complementary advantages of different RATs can be exploited. To address this problem, we formulate the traffic splitting as a long-term energy efficiency (EE) maximization problem with respect to the time-varying channel state information (CSI). An equivalent transformation method is proposed to convert the long-term nonconvex EE maximization problem into a concave optimization. To reduce the computational complexity, we develop a dynamic traffic splitting (DTS) algorithm, which iterates only one time when the network state changes. Then, we use the definition of tracking error to describe the difference between the DTS and the target optimal traffic splitting solution. After that, an adaptive-compensation traffic splitting (ACTS) algorithm is proposed to offset the tracking error so as to enhance the EE performance. More specifically, we give a sufficient condition for significantly eliminating the tracking errors of the ACTS algorithm. Simulation results show that the proposed ACTS algorithm obtains the EE performance comparable with the optimal solution at the overhead of only a single iteration at each timeslot of the network state acquisition.</P>