Large scale federated analytics and differential privacy budget preservation
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Ulhoa Avelar Stolet, Matheus
issued: 2022-09-30T07:00:00Z
issued: 2021
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: This thesis presents two contributions. The first contribution deals with the problem of siloed data collection and prohibitive data acquisition costs. These costs limit the size and diversity of datasets used in health research. Access to larger and more diverse datasets improves the understanding of disease heterogeneity and facilitates inference of relationships between surgical and pathological findings with symptomatic indicators and outcomes. Unfortunately, freely enabling access to these datasets has the potential of leaking private information, such as medical records, even when these datasets have been stripped of personally identifiable information. In the first part of this thesis, we present LEAP, a data analytics platform with support for federated learning. LEAP allows users to analyze data distributed across multiple institutions in a private and secure manner, without leaking sensitive patient information. LEAP achieves this through an infrastructure that maintains privacy by design and brings the computation to the data, instead of bringing the data to the computation. LEAP adds an overhead of up to 2.5X, training Resnet-18 with 15 participating sites, when compared to a centralized model. Despite this overhead, LEAP achieves convergence of the model’s accuracy within 20% of the time taken for the centralized model to converge. One of the techniques used by LEAP to preserve the privacy of sensitive queries is differential privacy. Successive DP queries to a dataset depletes the privacy budget. When the privacy budget is depleted, data curators must block access to the underlying dataset to prevent private information from leaking. In the second part of this thesis, we present a system called the SmartCache. The SmartCache optimizes the use of the privacy budget by interpolating old query results to help answer new queries using a synthetic dataset. Queries answered from the synthetic dataset have a smaller privacy cost, so more queries can be answer…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/79670
note: Large Scale Federated Analytics and Differential PrivacyBudget PreservationbyMatheus Ulhoa Avelar StoletBA. Computer Science, The University of British Columbia, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Computer Science)The University of British Columbia(Vancouver)August 2021© Matheus Ulhoa Avelar Stolet, 2021The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Large Scale Federated Analytics and Differential Privacy Budget Preser-vationsubmitted by Matheus Ulhoa Avelar Stolet in partial fulfillment of the require-ments for the degree of Master of Sciencein Computer Science.Examining Committee:Ivan Beschastnikh, Computer Science, UBCCo-SupervisorAline Talhouk, Medicine, UBCCo-SupervisoriiAbstractThis thesis presents two contributions. The first contribution deals with theproblem of siloed data collection and prohibitive data acquisition costs. These costslimit the size and diversity of datasets used in health research. Access to larger andmore diverse datasets improves the understanding of disease heterogeneity and fa-cilitates inference of relationships between surgical and pathological findings withsymptomatic indicators and outcomes. Unfortunately, freely enabling access tothese datasets has the potential of leaking private information, such as medicalrecords, even when these datasets have been stripped of personally identifiable in-formation.In the first part of this thesis, we present LEAP, a data analytics platform withsupport for federated learning. LEAP allows users to analyze data distributedacross multiple institutions in a private and secure manner, without leaking sen-sitive patient information. LEAP achieves this through an infrastructure that main-tains privacy by design and brings the computation to the data, instead of bringingthe data to th…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0401936
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Beschastnikh, Ivan
contributor: Talhouk, Aline
type: Text
identifier URI: http://hdl.handle.net/2429/79670