Against Signed Graph Deanonymization Attacks on Social Networks

Gao, Jianliang; Wang, Jianxin; He, Jing; Yan, Feifei

doi:10.1007/s10766-017-0546-6

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…Therefore, we plan to explore incentive mechanisms to encourage idle UEs to participate in outband D2D relaying services. In addition, the research combining incentive mechanism with the social awareness method [53] and the trust mechanism for throughput improvement is also an interesting future direction.…”

Section: Discussionmentioning

confidence: 99%

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Gui

Zhou

2018

J Wireless Com Network

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Device-to-device (D2D) communication is a promising option for the fifth generation (5G) mobile communication network to reduce energy consumption and increase throughput, which makes high throughput applications possible. Also, the improvement for energy and spectrum efficiency is critical in such applications. Without occupation of cellular spectrum resources, outband D2D communication is increasingly applied to high throughput applications to increase spectrum supply. However, energy efficiency is still a key issue that needs to be addressed. Moreover, the overall energy efficiency in cellular networks is severely limited by cell-edge devices. Therefore, in this paper, we apply multi-hop relay-aided outband D2D communication to cellular networks and propose a game-based power adjustment method to address throughput optimization problem. Firstly, we model an interaction relationship of power adjustment for each transmission path as a potential game, where a new utility function is designed for each player (i.e., the receiving end of a transmission path) to evaluate its action gain and to determine whether taking action or not. And then, it is proved that the utility function is an ordinal potential function (OPF) and the game of power adjustment is an ordinal potential game (OPG), which guarantees the convergence of game decision process. Moreover, we propose a new game decision algorithm, which has quicker convergence speed than the existing typical algorithm. In addition, we design a networkassisted distributed processing architecture for solving throughput optimization problem, including receiving mode selection, verification for relay selection, and transmission power adjustment, which can ease the burden of centralized processing. The experimental results show that our scheme is superior to the existing typical work in terms of throughput, delay, energy efficiency, continuous service ability, and convergence performance.

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Section: Discussionmentioning

confidence: 99%

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Gui

Zhou

2018

J Wireless Com Network

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“…All the sensor node has the same effective transmission radius . The same as most networks, the shortest routing protocol is used [23]. The nodes with the same number of hops from the sink node are in the same tier, so that the network can be divided into ⌈ / ⌉ tiers of the same width (0 < ≤ ).…”

Section: The Network Modelmentioning

confidence: 99%

“…However, because of the volatility of the wireless communication in network, the data packets in the wireless communication have a certain loss rate, which leads to the loss of each transmission of the data packets. This greatly reduces the probability of a node that is far from the sink node sending a packet to reach a sink node through multihop communication [23,24]. Therefore, how to ensure reliable data collection in wireless sensor networks is also a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

Teng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross-layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of nearsink area is high and that in far-sink area is low, a cross-layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission-only mechanism.

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“…In big data era [23], graph computing is widely used in different fields such as social networks [24], sensor networks [25,26], internet-of-things [27,28], and cellular networks [29]. Therefore, there is urgent demand for improving the performance of big graph processing, especially graph pattern matching.…”

Section: Related Workmentioning

confidence: 99%

Incremental Graph Pattern Matching Algorithm for Big Graph Data

Zhang

Gao

2018

Scientific Programming

Self Cite

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Graph pattern matching is widely used in big data applications. However, real-world graphs are usually huge and dynamic. A small change in the data graph or pattern graph could cause serious computing cost. Incremental graph matching algorithms can avoid recomputing on the whole graph and reduce the computing cost when the data graph or the pattern graph is updated. The existing incremental algorithm PGC IncGPM can effectively reduce matching time when no more than half edges of the pattern graph are updated. However, as the number of changed edges increases, the improvement of PGC IncGPM gradually decreases. To solve this problem, an improved algorithm iDeltaP IncGPM is developed in this paper. For multiple insertions (resp., deletions) on pattern graphs, iDeltaP IncGPM determines the nodes' matching state detection sequence and processes them together. Experimental results show that iDeltaP IncGPM has higher efficiency and wider application range than PGC IncGPM.

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Against Signed Graph Deanonymization Attacks on Social Networks

Cited by 8 publications

References 18 publications

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

Incremental Graph Pattern Matching Algorithm for Big Graph Data

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