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dc.contributor.advisorSamuel Madden.en_US
dc.contributor.authorKoko,Famien A.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-12-05T18:07:20Z
dc.date.available2019-12-05T18:07:20Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123172
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 47-48).en_US
dc.description.abstractDifferential privacy has recently emerged as a robust framework for delivering privacy guarantees when performing data analysis. Researchers have developed a wide array of algorithms which can provide these privacy guarantees. These algorithms differ in their performance on different datasets. This thesis explores a system for selecting the best algorithm that achieves a target error, given a database and workload: "error-based algorithm selection". In particular, we explore whether this problem can be solved in a private matter, how accurate it can be, and if it can be done competitively. The system we propose approaches the problem by building models for differentially private algorithms which use features from the workload and dataset as well as the target error to predict an epsilon value that will achieve the target error. It then selects the algorithm which predicts the best epsilon and uses it to run the workload. We evaluate the system on 1D and 2D workloads with several different algorithms and datasets. The system predicts the best algorithm a large percentage of the time, usually only being beaten by one algorithm and selects an algorithm that is within the top 2 best algorithms a majority of the time. The individual algorithm models achieve target error within a reasonable margin. The results show this approach is viable for the error-based algorithm selection problem, solving it in a differentially private, algorithm agnostic and competitive manner..en_US
dc.description.statementofresponsibilityby Famien A. Koko.en_US
dc.format.extent48 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleError based algorithm selection for differential privacyen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1129391072en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-12-05T18:07:18Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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