[11] J. Wang, R. Wen, C. Wu, Y. Huang, and J. Xiong, "Fdgars: Fraudster detection via graph convolutional networks in online app review system," in Companion proceedings of the 2019 World Wide Web conference, 2019, pp. 310-316.
[12] N. Carlini, C. Liu, U. Erlingsson, J. Kos, and D. Song, "The secret sharer: Evaluating and testing unintended memorization in neural networks," in USENIX Security Symposium, vol. 267, 2019.
[13] C. Dwork, F. McSherry, K. Nissim, and A. Smith, "Calibrating noise to sensitivity in private data analysis," in Theory of cryptography conference. Springer, 2006, pp. 265-284.
[14] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 308–318.
[15] A. Daigavane, G. Madan, A. Sinha, A. G. Thakurta, G. Aggarwal, and P. Jain, "Node-level differentially private graph neural networks," arXiv preprint arXiv:2111.15521, 2021.
[16] E. Chien, C. Pan, and O. Milenkovic, “Certified graph unlearning,” arXiv preprint arXiv:2206.09140, 2022.
[17] S. Sajadmanesh, A. S. Shamsabadi, A. Bellet, and D. Gatica-Perez, "Gap: Differentially private graph neural networks with aggregation perturbation," in 32nd USENIX Security Symposium (USENIX Security 23). Anaheim, CA: USENIX Association, Aug. 2023.
[18] Y. Deshpande, S. Sen, A. Montanari, and E. Mossel, "Contextual stochastic block models," Advances in Neural Information Processing Systems, vol. 31, 2018.
[19] E. Chien, J. Peng, P. Li, and O. Milenkovic, "Adaptive universal generalized pagerank graph neural network," in International Conference on Learning Representations, 2021. [Online]. Available: https://openreview.net/forum?id=n6jl7fLxrP
[20] A. D. Sarwate and K. Chaudhuri, "Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data," IEEE signal processing magazine, vol. 30, no. 5, pp. 86-94, 2013.
[21] R. Bassily, A. Smith, and A. Thakurta, "Private empirical risk minimization: Efficient algorithms and tight error bounds," in 2014 IEEE 55th annual symposium on foundations of computer science. IEEE, 2014, pp. 464-473.
[22] R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, 2015, pp. 1310-1321.
[23] X. Wu, F. Li, A. Kumar, K. Chaudhuri, S. Jha, and J. Naughton, “Bolt-on differential privacy for scalable stochastic gradient descent-based analytics,” in Proceedings of the 2017 ACM International Conference on Management of Data, 2017, pp. 1307–1322.
[24] A. Sala, X. Zhao, C. Wilson, H. Zheng, and B. Y. Zhao, "Sharing graphs using differentially private graph models," in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference, 2011, pp. 81-98.
[25] Z. Qin, T. Yu, Y. Yang, I. Khalil, X. Xiao, and K. Ren, "Generating synthetic decentralized social graphs with local differential privacy," in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017, pp. 425-438.
[26] M. Hay, C. Li, G. Miklau, and D. Jensen, "Accurate estimation of the degree distribution of private networks," in 2009 Ninth IEEE International Conference on Data Mining. IEEE, 2009, pp. 169-178.
[27] A. Epasto, V. Mirrokni, B. Perozzi, A. Tsitsulin, and P. Zhong, "Differentially private graph learning via sensitivity-bounded personalized pagerank," Advances in Neural Information Processing Systems, vol. 35, pp. 22617-22627, 2022.
[28] T. T. Mueller, D. Usynin, J. C. Paetzold, D. Rueckert, and G. Kaissis, "Sok: Differential privacy on graph-structured data," arXiv preprint arXiv:2203.09205, 2022.
[29] F. Wu, Y. Long, C. Zhang, and B. Li, "Linkteller: Recovering private edges from graph neural networks via influence analysis," in 2022 IEEE Symposium on Security and Privacy (SP). IEEE, 2022, pp. 2005-2024.
[30] Q. Zhang, J. Ma, J. Lou, C. Yang, and L. Xiong, "Towards training graph neural networks with node-level differential privacy," arXiv preprint arXiv:2210.04442, 2022.