Quantifying Privacy Leakage in Graph Embedding-Reference-Cited by-同舟云学术

Quantifying Privacy Leakage in Graph Embedding

Published:2020-12-07 Issue: Volume: Page:
ISSN:
Container-title:MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
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Author:

Duddu Vasisht¹,Boutet Antoine¹,Shejwalkar Virat²

Affiliation:

1. Lyon and 318

2. Amhrest and 318

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3448891.3448939

Reference48 articles.

1. Martin Abadi Andy Chu Ian Goodfellow H. Brendan McMahan Ilya Mironov Kunal Talwar and Li Zhang. 2016. Deep Learning with Differential Privacy. In CCS. 308––318. Martin Abadi Andy Chu Ian Goodfellow H. Brendan McMahan Ilya Mironov Kunal Talwar and Li Zhang. 2016. Deep Learning with Differential Privacy. In CCS. 308––318.

2. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers

3. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML.

4. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

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