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Obfuscating Community Structure in Complex Network With Evolutionary Divide-and-Conquer Strategy
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PRE
AI
DOI：10.1109/TEVC.2023.3242051.png)
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摘要
En 中文
As the number of social network users grows exponentially with increasingly complex profiles, community detection algorithms play a critical role in user portrait analysis. The associated privacy concerns, however, have not sufficiently received the attention that it deserves. In this work, we investigate methods for obfuscating the original community structure by modifying a small number of connections imperceptibly so as to protect the privacy of users. The existing evolutionary models have some successes in this type of NP-hard problem but can only be applied to small-scale datasets, rendering them inadequate for real-world applications. To alleviate this problem, we propose an original and novel CoeCo, a cooperative evolutionary community obfuscation model. In CoeCo, we leverage the divide-and-conquer strategy and put forward a co-evolutionary optimization algorithm suitable for community structure, in which two different fitness functions promote each other to find the optimal edge set. In addition, the motif hypergraph and permanence are used to improve population initialization. The experimental results indicate that our proposed method can achieve excellent efficacy in obfuscating community structure and also greatly reduces running time.
Keyword:
Privacy
Biological system modeling
Task analysis
Partitioning algorithms
Image edge detection
Social networking (online)
Measurement
Community detection
community obfuscation
cooperative co-evolution
data privacy
social network
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
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