基于DeepLink的社交网络去匿名方法

doi:10.11959/j.issn.2096-109x.2020045

Chinese Journal of Network and Information Security ›› 2020, Vol. 6 ›› Issue (4): 104-108.doi: 10.11959/j.issn.2096-109x.2020045

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De-anonymiation method for networks based on DeepLink

Pei WANG,Yan JIA,Aiping LI(),Qianyue JIANG

College of Computer,National University of Defense Technology,Changsha 410073,China

Revised:2019-09-17 Online:2020-08-15 Published:2020-08-13
Supported by:
The National Key R＆D Program of China(2017YFB0802204);The National Key R＆D Program of China(2016YFB0800303);The National Key R＆D Program of China(2017YFB0803301);The National Key R＆D Program of China(2016QY03D0603);The National Key R＆D Program of China(2016QY03D0601);The National Key R＆D Program of China(2016QY01W0101);The National Natural Science Foundation of China(61732004);The National Natural Science Foundation of China(61732022);The National Natural Science Foundation of China(61502517);The National Natural Science Foundation of China(61472433);The National Natural Science Foundation of China(61672020);The National Natural Science Foundation of China(U1803263);DongGuan Innovative Research Team Program(2018607201008)

Abstract

Abstract:

Existing de-anonymization technologies are mainly based on the network structure.To learn and express network structure is the key step of de-anonymization.The purpose of the user identity linkage is to detect the same user from different social networking platforms.DeepLink is a cross-social network user alignment technology.It learns the structural of the social networks and align anchor nodes through deep neural networks.DeepLink was used to identify de-anonymization social networks,and the results outperforms the traditional methods.

Key words: anonymization, de-anonymization, privacy, social network, graph data

CLC Number:

TP183

Pei WANG,Yan JIA,Aiping LI,Qianyue JIANG. De-anonymiation method for networks based on DeepLink[J]. Chinese Journal of Network and Information Security, 2020, 6(4): 104-108.

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References 15

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维度			准确率
维度	P@1	P@5	P@10P@15	P@30	P@100
400	66%	80%	83%83%	84%	84%
200	67%	81%	83%84%	85%	85%
100	78%	88%	90%91%	91%	91%
50	67%	81%	84%85%	85%	85%

方法				准确率
方法	P@1	P@5	P@10		P@15	P@30	P@100
本文方法	78%	88%	90%		91%	91%	91%
DeepLink	33%	57.4%	64.9%		67.2%	69.4%	69.8%

方法	准确率
本文方法	78%
Ji^[7]	40%
Nilizadeh^[8]	78%

De-anonymiation method for networks based on DeepLink

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