# American Institute of Mathematical Sciences

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November  2018, 1(4): 369-382. doi: 10.3934/mfc.2018018

## Relay selection based on social relationship prediction and information leakage reduction for mobile social networks

 1 School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, China 2 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China 3 The School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China 4 Computer Science, The George Washington University, Washington DC, USA

* Corresponding author: Gaofei Sun

Received  July 2018 Revised  August 2018 Published  December 2018

Fund Project: The first author is supported by National Natural Science Foundation of China (61602062) and the Natural Science Foundation of Jiangsu Province (BK20160410).

Despite the extensive study on relay selection in mobile social networks (MSNs), few work has taken both transmission latency (i.e. efficiency) and information leakage probability (i.e. security) into consideration. Therefore we target on designing an efficient and secure relay selection algorithm to enable communication among legitimate users while reducing the information leakage probability to other users. In this paper, we propose a novel mobility model for MSN users considering both the randomness and the sociality of the movements, based on which the social relationship among users, i.e. the meeting probabilities among the users, are predicted. Taken both efficiency and security into consideration, we design a network formation game based relay selection algorithm by defining the payoff functions of the users, designing the game evolving rules, and proving the stability of the formed network structure. Extensive simulation is conducted to validate the performance of the relay selection algorithm by using both synthetic trace and real-world trace. The results show that our algorithm outperforms other algorithms by trading a balance between efficiency and security.

Citation: Xiaoshuang Xing, Gaofei Sun, Yong Jin, Wenyi Tang, Xiuzhen Cheng. Relay selection based on social relationship prediction and information leakage reduction for mobile social networks. Mathematical Foundations of Computing, 2018, 1 (4) : 369-382. doi: 10.3934/mfc.2018018
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##### References:
A toy example of user $i$'s movement
The power law distribution under different $k$
Performance comparison in the real trace. (a) Comparing the A-Latency performance. (b) Comparing the A-MLP performance
Simulation Settings
 Parameter Meaning Setting $k_r$ The exponent of the power law distribution for $\zeta_{i\tau}$ 1.7 $k_l$ The exponent of the power law distribution for $p_i$ 3 $C_l$ The maximum value of $p_i$ 0.6 $R_d$ The radius of the communication range 6m $\epsilon$ The length of the time interval within which users keep their moving direction and speed unchanged 30s $\mu$ The mean of the normal distribution for users' speed 1.4 $\sigma$ The standard deviation of the normal distribution for users' speed $\frac{\mu}{3}$
 Parameter Meaning Setting $k_r$ The exponent of the power law distribution for $\zeta_{i\tau}$ 1.7 $k_l$ The exponent of the power law distribution for $p_i$ 3 $C_l$ The maximum value of $p_i$ 0.6 $R_d$ The radius of the communication range 6m $\epsilon$ The length of the time interval within which users keep their moving direction and speed unchanged 30s $\mu$ The mean of the normal distribution for users' speed 1.4 $\sigma$ The standard deviation of the normal distribution for users' speed $\frac{\mu}{3}$
Simulation Results
 ESRS Relation Leakage Rand A-Latency 16.2 15.4 17.9 30.4 A-MLP 0.38 0.63 0.35 0.72
 ESRS Relation Leakage Rand A-Latency 16.2 15.4 17.9 30.4 A-MLP 0.38 0.63 0.35 0.72
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