Privacy protection in cross-platform recommender systems: techniques and challenges

Zewei Sun1, Zining Wang1, Yanwei Xu2
1School of Computer Science, Qufu Normal University, Qufu, China
2College of Intelligence and Computing, Tianjin University, Tianjin, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

Luo, J., Yi, X., Han, F., et al. (2022). An efficient privacy-preserving recommender system in wireless networks. Wireless Networks. https://doi.org/10.1007/s11276-022-03130-6.

Kong, L., Li, G., Rafique, W., Shen, S., He, Q., Khosravi, M. R., Wang, R., & Qi, L. (2022). Time-aware Missing Healthcare Data Prediction based on ARIMA Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, DOI: https://doi.org/10.1109/TCBB.2022.3205064.

Jiang, L., Shi, L., Liu, L., et al. (2022). User interest community detection on social media using collaborative filtering. Wireless Networks, 28, 1169–1175.

Fan Wang, L., Wang, G., Li, Y., Wang, C., Lv, L., & Qi (2022). Edge-cloud-enabled Matrix Factorization for Diversified APIs recommendation in Mashup Creation. World Wide Web Journal, 25(5), 1809–1829.

Nguyen, D., Ding, M., Pathirana, P., & Seneviratne, A. (2019). User privacy in Recommendation Systems for internet of things. Ieee Access : Practical Innovations, Open Solutions, 7, 54745–54759.

FanWang, H., Zhu, G., Srivastava, S., Li, M. R., & Khosravi (2022). Lianyong Qi. Robust collaborative filtering recommendation with user-Item-Trust Records. IEEE Transactions on Computational Social Systems, 9(4), 986–996.

Zhou, X., Liang, W., Wang, K., Yan, Z., Yang, L. T., Wei, W., Ma, J., & Jin, Q. (Apr. 2023). Decentralized P2P Federated Learning for privacy-preserving and resilient Mobile Robotic Systems. IEEE Wireless Communications, 30(2), 82–89. https://doi.org/10.1109/MWC.004.2200381.

Lingzhen Kong, L., Wang, W., Gong, C., Duan, Y. Y., & Qi, L. (2022). LSH-aware Multitype Health Data Prediction with Privacy Preservation in Edge Environment. World Wide Web Journal, 25(5): 1793–1808.

Zhou, X., Liang, W., Wang, K., & Yang, L. T. (2021). Deep correlation mining based on hierarchical hybrid networks for heterogeneous Big Data Recommendations. IEEE Transactions on Computational Social Systems, 8(1), 171–178.

Wang, F., Li, G., Wang, Y., Rafique, W., Khosravi, M. R., Liu, G., Liu, Y., & Qi, L. (2022). Privacy-aware traffic Flow Prediction based on multi-party Sensor Data with Zero Trust in Smart City. ACM Transactions on Internet Technology. https://doi.org/10.1145/3511904.

Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2019). Collaborative filtering recommender systems. The adaptive web (pp. 291–324). Springer.

Wang, F., Xu, Z., & Shi, J. (2020). Predicting purchase behaviors from social media images with privacy considerations. IEEE Transactions on Knowledge and Data Engineering.

Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2020). Seven guiding scenarios for information visualization evaluation. IEEE Transactions on Visualization and Computer Graphics, 24(1), 489–502.

Chen, C., Li, C., & Duan, Y. (2022). Mobile healthcare data mining for sport item recommendation in edge-cloud collaboration. Wireless Networks. https://doi.org/10.1007/s11276-022-03059-w.

Lianyong Qi, W., Lin, X., Zhang, W., Dou, X., Xu, J., & Chen (2023). A correlation graph based Approach for Personalized and compatible web APIs recommendation in mobile APP development. IEEE Transactions on Knowledge and Data Engineering, 35(6), 5444–5457.

Li, B., Zhang, Y., Wang, T., & Cai, Z. (2019). Catching the rat by its tail: ProxRank-based shilling detection in recommender systems. IEEE Transactions on Knowledge and Data Engineering, 32(11), 2153–2166.

Yu, H., Yang, Z., Yao, H., & Yang, Z. (2019). Privacy-preserving collaborative filtering: A survey. ACM Computing Surveys (CSUR). https://doi.org/10.48550/arXiv.2003.08343.

Yihong Yang, X., Yang, M., Heidari, G., Srivastava, M. R., & Khosravi (2022). Lianyong Qi. ASTREAM: Data-Stream-Driven Scalable Anomaly detection with Accuracy Guarantee in IIoT Environment. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2022.3157730.

Shengqi Wu, S., Shen, X., Xu, Y., Chen, X., Zhou, D., Liu, X., Xue, L., & Qi (2023). Popularity-aware and diverse web APIs recommendation based on correlation graph. IEEE Transactions on Computational Social Systems, 10(2), 771–782.

Sweeney, L. (2019). Differential privacy. Harvard University.

Lee, J., Sun, M., & Lebanon, G. (2021). Axiomatic comparison of generative models for privacy-preservation in recommender systems. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 13, pp. 11557–11564).

McSherry, F. (2019). Privacy integrated queries: An extensible platform for privacy-preserving data analysis. Communications of the ACM, 53(9), 89–97.

Jiang, X., Zhang, J., Zhao, Y., He, S., & Liu, Y. (2020). Federated recommendation system with differential privacy. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 12177–12184).

Yang Liu Chen Tong, Q. Y. T. Y. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1–19.

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., & D’Oliveira, R. G. L. (2019). Advances and Open Problems in Federated Learning. arXiv preprint arXiv:1912.04977.

Lyu, L., Yu, H., & Yang, Q. (2020). Threats to Federated Learning: A Survey. arXiv preprint arXiv:2003.02133.

Nakamoto, S. (2019). Bitcoin: A peer-to-peer electronic cash system. Manubot.

Zhu, H., Zhou, Y., & Leung, V. C. (2020). Anonymity-Based Privacy-Preserving Data Reporting for Participatory Sensing. IEEE Internet of Things Journal, 7(2), 1196–1206.

Kim, H., Park, J., Bennis, M., Kim, S. L., Kim, D. H., & Choi, S. (2020). Blockchained On-Device Federated Learning. IEEE Communications Letters, 24(6), 1279–1283.

Dinh, T. T. A., Liu, R., Zhang, M., Chen, G., Ooi, B. C., & Wang, J. (2020). Untangling Blockchain: A Data Processing View of Blockchain Systems. IEEE Transactions on Knowledge and Data Engineering, 32(7), 1414–1433.

Zhang, R., Xue, R., & Liu, L. (2020). Security and privacy on Blockchain. ACM Computing Surveys (CSUR), 52(3), 1–34.

Breschi, S., Catalini, C., & Claudel, M. (2020). Blockchain and AI: A Primer. arXiv preprint arXiv:2001.07466.

Zhang, J., Liu, P., Wang, F., & Xu, J. (2020). A survey on user identity linkage. ACM Computing Surveys (CSUR), 53(2), 1–36.

Liu, L., Tang, J., Han, J., Jiang, M., & Yang, S. (2019). Personalized Click Model through Collaborative Filtering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 5282–5289).

Zhang, J., Zhang, Y., Zhang, F., Liu, C., & Xu, J. (2021). Unlink: A Rethinking of User Identity Linkage. In Proceedings of the Web Conference 2021 (pp. 313–324).

Zhu, H., Xiong, H., Ge, Y., & Chen, E. (2020). Differentially Private Bayesian Optimization. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, pp. 5005–5012).

Chen, L., Xu, Z., Zhang, H., & Zhao, Z. (2020). FedHealth: A Federated transfer learning Framework for Wearable Healthcare. IEEE Intelligent Systems, 35(4), 83–93.

Li, H., Wu, D., Wang, W., & Li, Z. (2020). Differential Privacy Preservation in Deep Learning: A Survey. arXiv preprint arXiv:2006.03234.

Kumar, M., Goyal, P., Varma, V., & Dahiya, K. (2019). Interpretable recommendation via attract, repel and explain neural networks. In Proceedings of the Web Conference 2019 (pp. 619–628).

Yang, X., Guo, Y., Liu, Y., & Steck, H. (2019). Sample-efficient deep learning for click-through rate prediction. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 13–21).

Pan, S., Yang, Q., & Duan, Y. (2020). Learning Transferable Knowledge across Domains with Factorization Machines. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, pp. 3150–3157).

Wang, S., Tang, J., Aggarwal, C., Liu, H., & Chang, Y. (2020). Linked Document Embedding for Classification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, pp. 6181–6188).

Zhang, H., Li, Q., & Zhang, Y. (2020). Differentiated privacy-preserving collaborative filtering. IEEE Transactions on Knowledge and Data Engineering, 32(9), 1758–1771.

Li, H., Jiang, L., Liu, Z., & Yang, Y. (2020). Federated Learning for Recommendation with Local Models and Feature Sharing. In Proceedings of the 28th ACM International Conference on Multimedia (pp. 3129–3137).

Chen, F., Pan, Z., Zhang, B., Xu, D., & Zeng, E. (2020). CLUE: A cluster-based Approach to consecutive localization for ubiquitous indoor environments. IEEE Internet of Things Journal, 7(10), 9710–9720.

Liu, C., Zhu, L., Wang, H., & Xu, W. (2020). Privacy-preserving personalized recommendation: An experimental study. Information Sciences, 509, 237–255.

Shi, S., Wang, Q., Xu, P., & Chu, X. (2021). Federated Learning for internet of things: Recent advances, taxonomy, and Open Challenges. IEEE Internet of Things Journal.

Jia, Y., Zhao, L., & Zou, P. (2020). A survey on user privacy and Fairness in Online Recommendations. Ieee Access : Practical Innovations, Open Solutions, 8, 130118–130131.

Chen, L., Zhou, J., Wang, B., & Li, N. (2021). Personalized privacy-preserving Social Recommendation. IEEE Transactions on Knowledge and Data Engineering.

Kumar, M., Goyal, P., & Varma, V. (2020). Interpretable Real-Time Bundle Recommendation Framework. In Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 298–306).

Li, P., Wang, H., Wang, C., & Zhou, Y. (2020). Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. arXiv preprint arXiv:2010.02565.

Cao, J., Chen, B., Liu, T., & Chen, C. (2020). Multi-Hop Federated learning for privacy Protection and Accuracy Improvement. IEEE Transactions on Industrial Informatics.

Li, Y., Wang, J., & Cai, Z. (2021). Personalized privacy-preserving Social Recommendation. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1396–1410.

Agrawal, S., & Kiayias, A. (2020). Privacy-preserving machine learning: Threats and solutions. IEEE Security & Privacy, 18(2), 26–35.

Jiang, Z., Li, X., Shang, S., & Liu, Y. (2021). Understanding the Trade-off between personalization and privacy: A study of user preferences and perception. Information Processing & Management, 58(2), 102357.

Zhang, Y., Dai, W., & Xu, Q. (2020). Privacy-preserving Smart Metering with Authentication in a Fog Computing System. IEEE Transactions on Industrial Informatics, 16(7), 4754–4763.

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., & Rouayheb, S. E. (2020). Advances and Open Problems in Federated Learning. arXiv preprint arXiv:1912.04977.

Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., & Zhou, Y. (2020). Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology. In Proceedings of the Web Conference 2020 (pp. 1409–1418).

Liu, S., Liu, S., & Liu, J. (2021). Personalized privacy-preserving prediction. IEEE Transactions on Knowledge and Data Engineering.

Pham, T., Nguyen, M., Nguyen, D., Pathirana, P. N., & Seneviratne, A. (2021). Privacy-preserving techniques for Blockchain-Based IoT Systems: Integrations and Challenges. Ieee Access : Practical Innovations, Open Solutions, 9, 56993–57010.

Wang, Q., Xu, J., & Wang, Z. (2020). User-Specific Cross-Platform Identification: A Case Study on Browser Fingerprint. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2005–2008).

Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2020). The effect of online privacy information on Purchasing Behavior: An experimental study. Information Systems Research, 22(2), 254–268.

Zhu, H., Zhou, Y., & Xiong, H. (2021). Cross-platform identification of Anonymous identical users in multiple social media networks. IEEE Transactions on Knowledge and Data Engineering.

Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., & Hu, J. (2020). Blockchain for Secure Identity Management in IoT: A position paper. Ieee Access : Practical Innovations, Open Solutions, 8, 138598–138612.

Li, J., Luo, X., Zhang, Y., Zhang, P., Yang, C., & Liu, F. (2022). Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths. Digital Commun Netw, 8(4), 455–465.

Qi Zhang, Y., Wang, Z., Cai, X., & Tong (2022). Multi-stage online task assignment driven by offline data under spatio-temporal crowdsurcing. Digital Commun Netw, 8(4), 516–530.

Zhang, Q., Zhang, X., Hu, H., Li, C., Lin, Y., & Ma, R. (2022). Sports match prediction model for training and exercise using attention-based LSTM network. Digital Commun Netw, 8(4), 508–515.

Ajay Kumar, K., Abhishek, M. R., Ghalib, A., Shanka, X., & Cheng (2022). Intrusion detection and prevention system for an IoT environment. Digital Commun Netw, 8(4), 540–551.

Dengcheng, Y., Zhao, Y., Yang, Z., Jin, Y., & Zhang, Y. (2022). Privacy-preserving federated cross-domain recommendation. Digital Commun Netw, 8(4), 552–560.

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2018). Deep Learning based Recommender System: A Survey and New Perspectives, ACM Computing Surveys, Volume 52, Issue 125, Article No.5, pp 1–38.

Siyu Wang, X., Chen, D., Jannach, & Yao, L. (2023). Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR).

Yuanjiang Cao, X., Chen, L., Yao, X., Wang, & Zhang, W. E. (2020). Adversarial Attack and Detection on Reinforcement Learning based Recommendation System. The 43rd Annual ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).

Lu, H., He, X., Du, M., Ruan, X., Sun, Y., & Wang, K. (2020). ”Edge QoE: Computation Offloading With Deep Reinforcement Learning for Internet of Things,” in IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9255–9265, Oct. https://doi.org/10.1109/JIOT.2020.2981557.

Xu, Y., Qi, L., Dou, W. (2017). Privacy-preserving and scalable service recommendation based on simhash in a distributed cloud environment[J]. Complexity, :1–9.

He, X., Wang, K., Huang, H., Miyazaki, T., Wang, Y., & Guo, S. (2020). Green Resource Allocation based on deep reinforcement learning in content-centric IoT. ” in IEEE Transactions on Emerging Topics in Computing, 8(3), 781–796. https://doi.org/10.1109/TETC.2018.2805718.