Last update: 7월 2일

세미나 시각: 금요일 오전 10시, zoom meeting (id: TBA)

1. 7월 10일, 임창준,  Ji et al.2014, Differential Privacy and Machine Learning: a Survey and Review.

2. 7월 17일, 김영래, 

   • Dwork et al.2006, Calibrating noise to sensitivity in private data analysis.

   • Nissim et al.2007, Smooth Sensitivity and Sampling in Private Data Analysis.

3. 7월 24일, 박성오, Ren et al. 2018, LoPub: High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

4. 7월 31일, 김승규,

   • DworkRothblum2016, Concentrated Differential Privacy

   • BunSteinke2016, Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

5. 8월 7일, 구태현, Asi et al.2019, Element Level Differential Privacy: The Right Granularity of Privacy

6. 8월 14일, 조영현, 

   • Charest2010: how to analyze differentially private data, 

   • McClureReiter2012: Differential Privacy and Statistical Disclosure Risk Measures: An Investigation with Binary Synthetic Data

7. 8월 21일, 이성진, Hardt et al. 2012, A Simple and Practical Algorithm for Differentially Private Data Release

8월 28일. 현형진, Jiang et al.2015, Wishart Mechanism for Differentially Private Principal Components Analysis SmithThakurta2013, Differentially Private Model Selection via Stability Arguments and the Robustness of the Lasso 

1. TBA, 조성훈, Amin et al.2019, Differentially Private Covariance Estimation

1. Asymptotics of empirical eigenstructure for high dimensional spiked covariance (https://projecteuclid.org/euclid.aos/1497319697)

2. Asymptotic Theory of Eigenvectors for Large Random Matrices (https://arxiv.org/abs/1902.06846)

3. Summary of Yata's works (noise reduction, data transformation)

4. Bootstrapping spectral statistics in high dimensions (https://arxiv.org/abs/1709.08251)

5. Global testing against sparse alternatives under Ising models (https://projecteuclid.org/euclid.aos/1534492829)