Privacy Preserving GWAS Data Sharing

By Stephen E. Fienberg, Aleksandra B. Slavkovic, Caroline Uhler. 2011

103 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Public Health, Environmental and Occupational Health

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. Adam D. Smith
2. Stephen E. Fienberg
3. Vitaly Shmatikov
4. Jing Lei
5. Aleksandra B. Slavkovic
6. Daniel Kifer
7. Yu-Xiang Wang
8. Adam Groce

In Molecular Biology

1. Adam D. Smith
2. Jun Sakuma
3. Kazuya Kakizaki
4. Kazuto Fukuchi
5. Vitaly Shmatikov
6. Aaron Johnson
7. Aleksandra B. Slavkovic
8. Jing Lei

In Public Health, Environmental and Occupational Health

1. Adam D. Smith
2. Jun Sakuma
3. Kazuya Kakizaki
4. Kazuto Fukuchi
5. Jing Lei
6. Aaron Johnson
7. Vitaly Shmatikov
8. Aleksandra B. Slavkovic

Citations in the corpus

• Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “Other tools implement algorithms targeting specific applications like: location data [35], genomic data [27, 47], mobility data [37], and browser error reports [26].”
• Privacy-Preserving Statistical Analysis by Exact Logistic Regression (2015, background): “Although many protocols for privacy-preserving GWAS already exist (such as secure computation of χ(2)-statistics based on contingency tables [27]), they do not account for covariates that might be possible confounding factor in the model (e.”
• Differentially Private Nonparametric Hypothesis Testing (2019, background): “Several other papers, though they make asymptotic arguments on the uniformity of their p-values, have developed frameworks for private chi-squared tests specifically for the intent of genomewide association study (GWAS) data [13, 18, 34].”
• Differentially private model selection with penalized and constrained likelihood (2016, methodology): “, 2014; Karwa and Slavković, 2016), non-parametric density estimation (Wasserman and Zhou, 2010), hypothesis testing (Fienberg et al., 2011; Johnson and Shmatikov, 2013; Uhler et al., 2013; Yu et al., 2014; Karwa et al., 2014; Solea, 2014; Dwork et al., 2015; Sheffet, 2015; Wang et al., 2015; Gaboar”
• Differentially Private Chi-squared Test by Unit Circle Mechanism (2017, methodology): “The output perturbation method randomizes the test statistic to satisfy differential privacy (Fienberg et al., 2011; Yu et al., 2014; Uhler et al., 2013; Wang et al., 2015).”
• Improved Differentially Private Analysis of Variance (2019, background): “Many private chi-squared tests have been specifically motivated by genome-wide association studies (GWAS) [9, 20, 11].”
• Privacy-Preserving Data Exploration in Genome-Wide Association Studies (2013, methodology): “[8] assume that the analyst knows in advance the number of “interesting” SNPs and the statistical tests to use.”