Location privacy-preserving recommendation scheme based on federated graph neural network

doi:10.11772/j.issn.1001-9081.2024010044

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (1): 136-143.DOI: 10.11772/j.issn.1001-9081.2024010044

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Location privacy-preserving recommendation scheme based on federated graph neural network

Liang ZHU¹, Jingzhe MU¹, Hongqiang ZUO², Jingzhong GU²(), Fubao ZHU¹

^1.School of Computer Science and Technology，Zhengzhou University of Light Industry，Zhengzhou Henan 450001，China
^2.Shangu Cyber Security Technology Company Limited，Zhengzhou Henan 450000，China

Received:2024-01-17 Revised:2024-03-27 Accepted:2024-03-27 Online:2024-05-09 Published:2025-01-10
Contact: Jingzhong GU
About author:ZHU Liang， born in 1987， Ph. D.， associate professor. His research interests include smart recommendation， privacy protection.
MU Jingzhe， born in 1999， M. S. candidate. His research interests include smart recommendation， privacy protection.
ZUO Hongqiang， born in 1989. His research interests include network security.
ZHU Fubao， born in 1974， Ph. D.， professor. His research interests include geographic information system， artificial intelligence.
Supported by:
National Natural Science Foundation of China(61902361);Henan Province Key Research and Development Special Project(221111210500);Henan Postgraduate Joint Training Base Project(YJS2022JD08)

基于联邦图神经网络的位置隐私保护推荐方案

朱亮¹, 慕京哲¹, 左洪强², 谷晶中²(), 朱付保¹

^1.郑州轻工业大学计算机科学与技术学院，郑州 450001
^2.山谷网安科技股份有限公司，郑州 450000

通讯作者: 谷晶中
作者简介:朱亮（1987—），男，河南焦作人，副教授，博士，主要研究方向：智能推荐、隐私保护；
慕京哲（1999—），男，河南焦作人，硕士研究生，主要研究方向：智能推荐、隐私保护；
左洪强（1989—），男，河南郑州人，主要研究方向：网络安全；
朱付保（1974—），男，河南郑州人，教授，博士，主要研究方向：地理信息系统、人工智能。
基金资助:
国家自然科学基金资助项目(61902361);河南省重点研发专项(221111210500);河南省研究生联合培养基地项目(YJS2022JD08)

Abstract

Abstract:

Traditional location service recommendation schemes lack consideration of user preferences and potential social relationships， resulting in recommendation results that fail to meet user’s personalized needs. Graph Neural Networks （GNNs） are widely used in the field of location recommendation by its good graph structure data processing capabilities. However， the previous studies’ centralized data paradigm is easy to lead to the issue of location privacy leakage. Therefore， a Location Privacy-preserving Recommendation scheme based on Federated Graph Neural Network （FedGNN-LPR） was proposed. Firstly， the user’s social relationship embedding and Point-Of-Interest （POI） embedding were learned through the graph attention network. Secondly， a POI-based pseudo labelling model was developed to predict the number of user visits to an unknown location， so as to protect user privacy and alleviate the cold-start problem. Finally， a clustered federated learning strategy based on differential privacy was proposed to protect client interaction data and solve the problem of data heterogeneity. Experiments were conducted on two publicly available real datasets， and the results demonstrate that the proposed scheme is reduced by 7.89% and 9.29% respectively compared to the Federated Averaging （FedAvg） algorithm， and 2.32% and 2.75% respectively compared to the FL+HC algorithm， in terms of Mean Absolute Error （MAE） and Root Mean Square Error （RMSE）. Moreover， it is shown that FedGNN-LPR exhibits better performance on federated learning location recommendation. Therefore， FedGNN-LPR not only protects user location privacy， but also improves location recommendation performance.

Key words: Location-Based Social Network (LBSN), federated learning, graph attention network, pseudo label, location recommendation

摘要：

传统的位置服务推荐方案未考虑用户偏好及潜在社交关系等问题，导致推荐结果无法满足用户的个性化需求。图神经网络（GNN）凭借较好的图结构数据处理能力，被广泛用于位置推荐领域；然而，此前研究里中心化的数据范式容易造成位置隐私泄露的问题。因此，提出一种基于联邦图神经网络的位置隐私保护推荐方案（FedGNN-LPR）。首先，通过图注意力网络学习用户的社交关系嵌入和兴趣点（POI）嵌入；其次，建立基于POI的伪标签标注模型预测用户对未知位置的访问次数，以保护用户隐私并缓解冷启动问题；最后，提出基于差分隐私的聚类联邦学习策略保护客户端的交互数据并解决数据异质性问题。在两个公开的真实数据集上进行实验的结果表明，在平均绝对值误差（MAE）和均方根误差（RMSE）方面，所提方案比联邦平均（FedAvg）算法分别降低了7.89%和9.29%，比FL+HC算法分别降低了2.32%和2.75%；并且，所提方案在联邦学习位置推荐上展现出更好的性能。因此，FedGNN-LPR不仅能保护用户位置隐私，而且提高了位置推荐性能。

关键词: 基于位置的社交网络, 联邦学习, 图注意力网络, 伪标签, 位置推荐

CLC Number:

TP393.08

Liang ZHU, Jingzhe MU, Hongqiang ZUO, Jingzhong GU, Fubao ZHU. Location privacy-preserving recommendation scheme based on federated graph neural network[J]. Journal of Computer Applications, 2025, 45(1): 136-143.

朱亮, 慕京哲, 左洪强, 谷晶中, 朱付保. 基于联邦图神经网络的位置隐私保护推荐方案[J]. 《计算机应用》唯一官方网站, 2025, 45(1): 136-143.

Figures/Tables 10

Fig. 1 Basic flow of federated learning

Fig. 2 Overall overview of FedGNN-LPR

Fig. 3 Location and semantic labeling of user A

Fig. 4 Location and semantic labeling of user B

Fig. 5 Performance comparison of different schemes

Fig. 6 Performance indicators and clustering accuracy of FedGNN-LPR and FL+HC

Fig. 7 Influence of embedding size on performance of FedGNN-LPR

Fig. 8 Influence of number of pseudo POIs on performance of FedGNN-LPR

Fig. 9 MAE performance under different noise intensities and gradient clipping thresholds

Fig. 10 RMSE performance under different noise intensities and gradient clipping thresholds

References 36

1	YADAV V K， ANDOLA N， VERMA S， et al. P2LBS： privacy provisioning in location-based services ［J］. IEEE Transactions on Services Computing， 2023， 16（1）： 466-477.
2	XIONG X， QIAO S， HAN N， et al. Affective impression： sentiment-awareness POI suggestion via embedding in heterogeneous LBSNs ［J］. IEEE Transactions on Affective Computing， 2022， 13（1）： 272-284.
3	LIU Y， YIN M， ZHOU X. A collaborative filtering algorithm with intragroup divergence for POI group recommendation ［J］. Applied Sciences， 2021， 11（12）： No.5416.
4	ZHANG Y， YIN C， WU Q， et al. Location-aware deep collaborative filtering for service recommendation ［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2021， 51（6）： 3796-3807.
5	WANG W， QUAN Z， ZHAO S， et al. User-context collaboration and tensor factorization for GNN-based social recommendation ［J］. IEEE Transactions on Network Science and Engineering， 2023， 10（6）： 3320-3330.
6	刘欢，李晓戈，胡立坤，等.基于知识图谱驱动的图神经网络推荐模型［J］.计算机应用， 2021， 41（7）： 1865-1870.
	LIU H， LI X G， HU L K， et al. Knowledge graph driven recommendation model of graph neural network ［J］. Journal of Computer Applications， 2021， 41（7）： 1865-1870.
7	FAN W， MA Y， LI Q， et al. Graph neural networks for social recommendation ［C］// Proceedings of the 2019 World Wide Web Conference. New York： ACM， 2019： 417-426.
8	ZHANG J， LIU X， ZHOU X， et al. Leveraging graph neural networks for Point-of-Interest recommendations ［J］. Neurocomputing， 2021， 462： 1-13.
9	ALBRECHT J P. How the GDPR will change the world ［J］. European Data Protection Law Review， 2016， 2（3）： 287-289.
10	TRELEAVEN P， SMIETANKA M， PITHADIA H. Federated learning： the pioneering distributed machine learning and privacy-preserving data technology ［J］. Computer， 2022， 55（4）： 20-29.
11	LUO S， XIAO Y， SONG L. Personalized federated recommendation via joint representation learning， user clustering， and model adaptation ［C］// Proceedings of the 31st ACM International Conference on Information and Knowledge Management. New York： ACM， 2022： 4289-4293.
12	ZION E BEN， LERNER B. Identifying and predicting social lifestyles in people’s trajectories by neural networks ［J］. EPJ Data Science， 2018， 7： No.45.
13	LIU Y， WEI W， SUN A， et al. Exploiting geographical neighborhood characteristics for location recommendation ［C］// Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. New York： ACM， 2014： 739-748.
14	YIN M， LIU Y， ZHOU X， et al. A tensor decomposition based collaborative filtering algorithm for time-aware POI recommendation in LBSN ［J］. Multimedia Tools and Applications， 2021， 80（30）： 36215-36235.
15	CHEN M， LI W Z， QIAN L， et al. Next POI recommendation based on location interest mining with recurrent neural networks ［J］. Journal of Computer Science and Technology， 2020， 35（3）： 603-616.
16	HUANG L， MA Y， WANG S， et al. An attention-based spatiotemporal LSTM network for next POI recommendation ［J］. IEEE Transactions on Services Computing， 2021， 14（6）： 1585-1597.
17	WU S， ZHANG Y， GAO C， et al. GARG： anonymous recommendation of point-of-interest in mobile networks by graph convolution network ［J］. Data Science and Engineering， 2020， 5（4）： 433-447.
18	LIU J， CHEN Y， HUANG X， et al. GNN-based long and short term preference modeling for next-location prediction ［J］. Information Sciences， 2023， 629： 1-14.
19	VELIČKOVIĆ P， CUCURULL G， CASANOVA A， et al. Graph attention networks ［EB/OL］. ［2023-01-12］. .
20	KUANG L， TU S， ZHANG Y， et al. Providing privacy preserving in next POI recommendation for mobile edge computing ［J］. Journal of Cloud Computing， 2020， 9： No.10.
21	MENG S， QI L， LI Q， et al. Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems ［J］. Future Generation Computer Systems， 2019， 96： 324-335.
22	McMAHAN B， MOORE E， RAMAGE D， et al. Communication-efficient learning of deep networks from decentralized data ［C］// Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. New York： JMLR.org， 2017： 1273-1282.
23	WU C， WU F， LYU L， et al. A federated graph neural network framework for privacy-preserving personalization ［J］. Nature Communications， 2022， 13： No.3091.
24	DONG Q， LIU B， ZHANG X， et al. Sequential POI recommend based on personalized federated learning ［J］. Neural Processing Letters， 2023， 55： 7351-7368.
25	LI Z， BILAL M， XU X， et al. Federated learning-based cross-enterprise recommendation with graph neural networks ［J］. IEEE Transactions on Industrial Informatics， 2023， 19（1）： 673-682.
26	DING X， LI G， YUAN L， et al. Efficient federated item similarity model for privacy-preserving recommendation ［J］. Information Processing and Management， 2023， 60（5）： No.103470.
27	BRIGGS C， FAN Z， ANDRAS P. Federated learning with hierarchical clustering of local updates to improve training on non-IID data ［C］// Proceedings of the 2020 IEEE International Joint Conference on Neural Networks. Piscataway： IEEE， 2020： 1-9.
28	温菊屏，胡小生，林冬梅，等.基于参考节点嵌入的图可达性查询［J］.计算机应用， 2016， 36（7）： 1998-2005.
	WEN J P， HU X S， LIN D M， et al. Graph reachability query based on reference node embedding ［J］. Journal of Computer Applications， 2016， 36（7）： 1998-2005.
29	胡军，许正康，刘立，等.融合多粒度社区信息的网络嵌入方法［J］.计算机应用， 2022， 42（3）： 663-670.
	HU J， XU Z K， LIU L， et al. Network embedding method based on multi-granularity community information ［J］. Journal of Computer Applications， 2022， 42（3）： 663-670.
30	富坤，高金辉，赵晓梦，等.融合全局结构信息的拓扑优化图卷积网络［J］.计算机应用， 2022， 42（2）： 357-364.
	FU K， GAO J H， ZHAO X M， et al. Topology optimization based graph convolutional network combining with global structural information ［J］. Journal of Computer Applications， 2022， 42（2）： 357-364.
31	LIU Z， YANG L， FAN Z， et al. Federated social recommendation with graph neural network ［J］. ACM Transactions on Intelligent Systems and Technology， 2022， 13（4）： No.55.
32	张国鹏，陈学斌，王豪石，等.面向本地差分隐私的K-Prototypes聚类方法［J］.计算机应用， 2022， 42（12）： 3813-3821.
	ZHANG G P， CHEN X B， WANG H S， et al. K-Prototypes clustering method for local differential privacy ［J］. Journal of Computer Applications， 2022， 42（12）： 3813-3821.
33	ZHENG Y， XIE X， MA W Y GeoLife ： a collaborative social networking service among user， location and trajectory ［J］. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering， 2010， 33（2）： 32-39.
34	中国POI数据［DB/OL］. ［2023-10-22］. .
	China POI data ［DB/OL］. ［2023-10-22］. .
35	HE X， LIAO L， ZHANG H， et al. Neural collaborative filtering ［C］// Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva： International World Wide Web Conferences Steering Committee， 2017： 173-182.
36	CHAI D， WANG L， CHEN K， et al. Secure federated matrix factorization ［J］. IEEE Intelligent Systems， 2021， 36（5）： 11-20.

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Location privacy-preserving recommendation scheme based on federated graph neural network

基于联邦图神经网络的位置隐私保护推荐方案

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