{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/153906"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/153906","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Private Random Variate Sampling for Secure and Federated Polygenic Risk Scores","abstract":"Polygenic risk scores (PRS) are used to quantify the additive effect of single nucleotide polymorphisms (SNPs) on an individual’s genetic risk for developing a particular trait or condition. Collaborations between data centers are important for improving the statistical power and validity of PRS through larger, more genetically diverse datasets. However, owing to the privacy concerns inherent in genomic data, regulations restrict institutions’ capacity to share data. Using cryptography, we present a secure and federated implementation of a Monte Carlo algorithm for PRS, enabling collaborations that respect data regulations. To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest.","abstract_html":"Polygenic risk scores (PRS) are used to quantify the additive effect of single nucleotide polymorphisms (SNPs) on an individual’s genetic risk for developing a particular trait or condition. Collaborations between data centers are important for improving the statistical power and validity of PRS through larger, more genetically diverse datasets. However, owing to the privacy concerns inherent in genomic data, regulations restrict institutions’ capacity to share data. Using cryptography, we present a secure and federated implementation of a Monte Carlo algorithm for PRS, enabling collaborations that respect data regulations. To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest.","abstract_has_math":false,"creators":["Yen, Derek Jia-Wen"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Collaborations between data centers are important for improving the statistical power and validity of PRS through larger, more genetically diverse datasets. However, owing to the privacy concerns inherent in genomic data, regulations restrict institutions’ capacity to share data. Using cryptography, we present a secure and federated implementation of a Monte Carlo algorithm for PRS, enabling collaborations that respect data regulations. To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Private Random Variate Sampling for Secure and Federated Polygenic Risk Scores"]}]}],"canonical_facts":{"dc:contributor.advisor":["Berger, Bonnie","Cho, Hyunghoon"],"dc:contributor.department":["Massachusetts Institute of Technology. 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To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/153906"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","Copyright retained by author(s)"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:title":["Private Random Variate Sampling for Secure and Federated Polygenic Risk Scores"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:51Z"}