Differential Privacy Preserving Spectral Graph Analysis

By Yue Wang, Xintao Wu, Leting Wu. 2013

127 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dejing Dou
2. Nhathai Phan
3. Xintao Wu
4. S. Ji
5. Ting Wang
6. Han Hu
7. Yue Wang
8. Cheng Si

In Statistics and Probability

1. S. Ji
2. Ting Wang
3. Xintao Wu
4. Nhathai Phan
5. Han Hu
6. Dejing Dou
7. Yue Wang

Citations in the corpus

• Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016, background): “…in some particular analysis tasks, e.g., collaborative recommendation (McSherry and Mironov 2009), logistic regression (Chaudhuri and Monteleoni 2008), publishing contingency tables (Xiao, Wang, and Gehrke 2010), and spectral graph analysis (Wang, Wu, and Wu 2013) in social network analysis.”
• Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning (2017, background): “, data collection [19], spatiotemporal correlations [20, 21], data streams [22], stochastic gradient descents [23], recommendation [7], regression [6], online learning [24], publishing contingency tables [25], and spectral graph analysis [26].”
• Regression Model Fitting under Differential Privacy and Model Inversion Attack (2015, background): “…synthetic graph , 2013; Mir and Wright, 2009; Sala et al. , 2011 ] , computing graph properties such as degree distributions [ Hay et al. , 2009 ] and clustering coefficient [ Rastogi et al. , 2009; Wang et al. , 2012 ] , and spectral graph analysis [ Wang et al. , 2013 ] in social network analysis.”
• Preserving differential privacy in convolutional deep belief networks (2017, background): “…from both the theoretical perspective, e.g., [15, 35], and the application perspective, e.g., data collection [22], data streams [13], stochastic gradient descents [69], recommendation [49], regression [15], online learning [33], publishing contingency tables [74], and spectral graph analysis [72].”
• Differentially Private Releasing via Deep Generative Model (2018, methodology): “The existing work can be roughly categorized into supervised settings, such as logistic regression [5] and support vectormachine (SVM) [5, 31], and unsupervised settings, such as publishing histograms [37], releasing contingency tables [38], hypothesis testing [13], collaborative recommendation [42]”