Machine Learning-based Online Social Network Privacy Preservation

Author:

Gao Tianchong1,Li Feng2

Affiliation:

1. Southeast University, Nanjing, China

2. Indiana University - Purdue University Indianapolis, Indianapolis, IN, USA

Funder

National Science Foundation

National Science Foundation of China

Publisher

ACM

Reference46 articles.

1. Distributed large-scale natural graph factorization

2. Mikhail Belkin and Partha Niyogi . Laplacian eigenmaps and spectral techniques for embedding and clustering . In Advances in neural information processing systems , pages 585 -- 591 , 2002 . Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in neural information processing systems, pages 585--591, 2002.

3. Aleksandar Bojchevski and Stephan Günnemann . Adversarial attacks on node embeddings via graph poisoning . In Proceedings of the 36th International Conference on Machine Learning (ICML) , pages 695 -- 704 , 2019 . Aleksandar Bojchevski and Stephan Günnemann. Adversarial attacks on node embeddings via graph poisoning. In Proceedings of the 36th International Conference on Machine Learning (ICML), pages 695--704, 2019.

4. Correlated network data publication via differential privacy

5. Hanjun Dai , Hui Li , Tian Tian , Xin Huang , Lin Wang , Jun Zhu , and Le Song . Adversarial attack on graph structured data . In Proceedings of the 35th International Conference on Machine Learning (ICML) , pages 1115 -- 1124 , 2018 . Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. Adversarial attack on graph structured data. In Proceedings of the 35th International Conference on Machine Learning (ICML), pages 1115--1124, 2018.

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