Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy-Reference-Cited by-同舟云学术

Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy

Published:2019-11-06 Issue: Volume: Page:
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Container-title:Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Sun Haipei¹,Xiao Xiaokui²,Khalil Issa¹,Yang Yin³,Qin Zhan⁴,Wang Hui (Wendy)⁵,Yu Ting¹

Affiliation:

1. Qatar Computing Research Institute, Doha, Qatar

2. National University of Singapore, Singapore, Singapore

3. Hamad Bin Khalifa University, Doha, Qatar

4. Zhejiang University, Zhejiang, China

5. Stevens Institute of Technology, Hoboken, NJ, USA

Funder

National Research Foundation Prime Minister's Office Singapore

National Science Foundation (NSF) USA

Qatar National Research Fund Qatar

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3319535.3354253

Reference43 articles.

1. Local, Private, Efficient Protocols for Succinct Histograms

2. The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy

3. Differentially private data analysis of social networks via restricted sensitivity

4. Christian Borgs Jennifer Chayes and Adam Smith. 2015. Private graphon estimation for sparse graphs. In Advances in Neural Information Processing Systems . 1369--1377. Christian Borgs Jennifer Chayes and Adam Smith. 2015. Private graphon estimation for sparse graphs. In Advances in Neural Information Processing Systems . 1369--1377.

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