Research Article
Practical Privacy-Preserving Community Detection in Decentralized Weighted Networks
@INPROCEEDINGS{10.1007/978-3-031-64954-7_16, author={Tingxuan Han and Wei Tong and Jiacheng Niu and Sheng Zhong}, title={Practical Privacy-Preserving Community Detection in Decentralized Weighted Networks}, proceedings={Security and Privacy in Communication Networks. 19th EAI International Conference, SecureComm 2023, Hong Kong, China, October 19-21, 2023, Proceedings, Part II}, proceedings_a={SECURECOMM PART 2}, year={2024}, month={10}, keywords={Community detection Decentralized weighted networks Privacy-preserving Local differential privacy}, doi={10.1007/978-3-031-64954-7_16} }- Tingxuan Han
Wei Tong
Jiacheng Niu
Sheng Zhong
Year: 2024
Practical Privacy-Preserving Community Detection in Decentralized Weighted Networks
SECURECOMM PART 2
Springer
DOI: 10.1007/978-3-031-64954-7_16
Abstract
As one of the essential graph analysis tasks, community detection plays a crucial role in various applications. Since a graph can be viewed as a collection of users’ personal information and the relationships among them, it is essential to protect individuals’ privacy during the process of community detection. Weighted networks are networks with real-valued weights on edges, and the edge weights should be protected when performing community detection in weighted networks. In this paper, we study the privacy-preserving community detection problem for weighted networks where each node is a distributed user and there is no trusted third party. Directly applying homomorphic encryption to implement a secure version of community detection for the untrusted analyst will incur high communication and computational cost, while local perturbation methods, such as local differential privacy, may significantly degrade the accuracy. Therefore, we combine these two types of techniques and propose a practical privacy-preserving algorithm for community detection in weighted networks, by blending a tailored locally differentially private mechanism with a cryptographic component. The proposed method can provide provable privacy guarantees and satisfactory performance. Extensive experiments we conducted have demonstrated the accuracy and efficiency of the proposed algorithm in various cases.
