Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge

By Fei Yu, Zhanglong Ji. 2014

44 citations recorded by the source corpus.

In response to the growing interest in genome-wide association study (GWAS) data privacy, the Integrating Data for Analysis, Anonymization and SHaring (iDASH) center organized the iDASH Healthcare Privacy Protection Challenge, with the aim of investigating the effectiveness of applying privacy-preserving methodologies to human genetic data. This paper is based on a submission to the iDASH Healthcare Privacy Protection Challenge. We apply privacy-preserving methods that are adapted from Uhler et al. 2013 and Yu et al. 2014 to the challenge's data and analyze the data utility after the data are perturbed by the privacy-preserving methods. Major contributions of this paper include new interpretation of the χ2 statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods.

Subfields: Artificial Intelligence, Genetics, Pediatrics, Perinatology and Child 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. Or Sheffet
2. Aleksandra Korolova
3. Uri Stemmer
4. Kobbi Nissim
5. Amos Beimel
6. Jonathan Ullman
7. Vitaly Feldman
8. Mark Bun

In Genetics

1. Chloé-Agathe Azencott
2. Bonnie Berger
3. Sean K. Simmons

In Pediatrics, Perinatology and Child Health

1. Bonnie Berger
2. Sean K. Simmons
3. Chloé-Agathe Azencott

Citations in the corpus

• The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “It is also of core interest in genome-wide association studies (GWAS), where the inputs are genomes of patients a ff ected by a particular disease and the goal is to (approximately) identify disease-related genome locations and estimate their significance [3, 67, 15].”
• Realizing privacy preserving genome-wide association studies (2016, methodology): “Unlike previous work (Yu and Ji, 2014), we are able to achieve this while protecting the genomic data of all study participants.”
• Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, unknown): “This technique allows researchers access to genomic data while preserving every patient’s privacy (Jiang et al., 2014; Johnson and Shmatikov, 2013; Uhlerop et al., 2013; Yu et al., 2014a, 2014b; Yu and Ji, 2014; Chen et al., 2014; Zhao et al., 2015; Zhang et al., 2013).”
• Machine learning and genomics: precision medicine versus patient privacy (2018, background): “Nevertheless, recent work [40, 41, 42] has shown how to implement differential privacy for GWAS, making it possible for users to query a genetic database for the differentially private top k most associated SNPs.”