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Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation (2401.14583v1)

Published 26 Jan 2024 in cs.IR

Abstract: As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attractive and engaging places. However, the accurate recommendation capability relies on the powerful server collecting a vast amount of users' historical check-in data, posing significant risks of privacy breaches. Although several collaborative learning (CL) frameworks for POI recommendation enhance recommendation resilience and allow users to keep personal data on-device, they still share personal knowledge to improve recommendation performance, thus leaving vulnerabilities for potential attackers. Given this, we design a new Physical Trajectory Inference Attack (PTIA) to expose users' historical trajectories. Specifically, for each user, we identify the set of interacted POIs by analyzing the aggregated information from the target POIs and their correlated POIs. We evaluate the effectiveness of PTIA on two real-world datasets across two types of decentralized CL frameworks for POI recommendation. Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories. Furthermore, Local Differential Privacy (LDP), the traditional privacy-preserving method for CL frameworks, has also been proven ineffective against PTIA. In light of this, we propose a novel defense mechanism (AGD) against PTIA based on an adversarial game to eliminate sensitive POIs and their information in correlated POIs. After conducting intensive experiments, AGD has been proven precise and practical, with minimal impact on recommendation performance.

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References (43)
  1. Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers. Int. J. Secur. Netw. 10, 3 (sep 2015), 137–150. https://doi.org/10.1504/IJSN.2015.071829
  2. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation. In Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18.
  3. Air: Attentional intention-aware recommender systems. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 304–315.
  4. Sequence-aware factorization machines for temporal predictive analytics. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1405–1416.
  5. Try this instead: Personalized and interpretable substitute recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 891–900.
  6. Where You Like to Go Next: Successive Point-of-Interest Recommendation.. In International Joint Conference on Artificial Intelligence.
  7. Privacy in pharmacogenetics: An {{\{{End-to-End}}\}} case study of personalized warfarin dosing. In 23rd USENIX security symposium (USENIX Security 14). 17–32.
  8. Mitigating sybils in federated learning poisoning. arXiv preprint arXiv:1808.04866 (2018).
  9. Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback-Leibler Divergence based on Stator Current Measurements. IEEE Transactions on Industrial Electronics (2014).
  10. PREFER: Point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 5, 1 (2021), 1–25.
  11. Deep models under the GAN: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 603–618.
  12. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. In the 2018 World Wide Web Conference.
  13. Next Point-of-Interest Recommendation with Temporal and Multi-level Context Attention. 2018 IEEE International Conference on Data Mining (ICDM) (2018), 1110–1115.
  14. Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation. In IJCAI. 1491–1497.
  15. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. ACM.
  16. Personalized point-of-interest recommendation by mining users’ preference transition. In Acm International Conference on Conference on Information & Knowledge Management.
  17. Decentralized Collaborative Learning Framework for Next POI Recommendation. TOIS (2022).
  18. Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 423–432.
  19. Feature inference attack on model predictions in vertical federated learning. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 181–192.
  20. STAN: Spatio-Temporal Attention Network for Next Location Recommendation.
  21. J. Macqueen. 1967. Some methods for classification and analysis of multivariate observations. Proc. Symp. Math. Statist. and Probability, 5th 1 (1967).
  22. On the privacy of decentralized machine learning. arXiv preprint arXiv:2205.08443 (2022).
  23. Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster. 012017–.
  24. Data poisoning attacks against federated learning systems. In Computer Security–ESORICS 2020: 25th European Symposium on Research in Computer Security, ESORICS 2020, Guildford, UK, September 14–18, 2020, Proceedings, Part I 25. Springer, 480–501.
  25. User label leakage from gradients in federated learning. arXiv preprint arXiv:2105.09369 (2021).
  26. Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. In WWW ’20: The Web Conference 2020.
  27. Fast-adapting and Privacy-preserving Federated Recommender System. (2021).
  28. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security 15 (2020), 3454–3469.
  29. CoFiRank - Maximum Margin Matrix Factorization for Collaborative Ranking. In Neural Information Processing Systems.
  30. Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks. IEEE Transactions on Signal Processing 68 (2020), 4583–4596.
  31. Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation. In Uncertainty in Artificial Intelligence. PMLR, 261–270.
  32. Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach. In The world wide web conference. 2147–2157.
  33. GETNext: trajectory flow map enhanced transformer for next POI recommendation. In Proceedings of the 45th International ACM SIGIR Conference on research and development in information retrieval. 1144–1153.
  34. Hongzhi Yin and Bin Cui. 2016. Spatio-temporal recommendation in social media. Springer.
  35. Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In Proceedings of the 23rd ACM international conference on Multimedia. 819–822.
  36. On-Device Recommender Systems: A Comprehensive Survey. arXiv:2401.11441 [cs.IR]
  37. Lcars: a location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 221–229.
  38. Interaction-level membership inference attack against federated recommender systems. arXiv preprint arXiv:2301.10964 (2023).
  39. Federated unlearning for on-device recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 393–401.
  40. Graph embedding for recommendation against attribute inference attacks. In Proceedings of the Web Conference 2021. 3002–3014.
  41. Comprehensive privacy analysis on federated recommender system against attribute inference attacks. IEEE Transactions on Knowledge and Data Engineering (2023).
  42. Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation. 5877–5884.
  43. Keyword-aware continuous knn query on road networks. In 2016 IEEE 32Nd international conference on data engineering (ICDE). IEEE, 871–882.
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