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dc.contributor.advisorJohn N. Tsitsiklis and Kuang Xu.en_US
dc.contributor.authorXu, Zhi, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2017-10-30T15:29:28Z
dc.date.available2017-10-30T15:29:28Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/112054
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 107-108).en_US
dc.description.abstractWe propose and analyze two models to study an intrinsic trade-off between privacy and query complexity in online settings: 1. Our first private optimization model involves an agent aiming to minimize an objective function expressed as a weighted sum of finitely many convex cost functions, where the weights capture the importance the agent assigns to each cost function. The agent possesses as her private information the weights, but does not know the cost functions, and must obtain information on them by sequentially querying an external data provider. The objective of the agent is to obtain an accurate estimate of the optimal solution, x*, while simultaneously ensuring privacy, by making x* difficult to infer for the data provider, who does not know the agent's private weights but only observes the agent's queries. 2. The second private search model we study is also about protecting privacy while searching for an object. It involves an agent attempting to determine a scalar true value, x*, based on querying an external database, whose response indicates whether the true value is larger than or less than the agent's submitted queries. The objective of the agent is again to obtain an accurate estimate of the true value, x*, while simultaneously hiding it from an adversary who observes the submitted queries but not the responses. The main results of this thesis provide tight upper and lower bounds on the agent's query complexity (i.e., number of queries) as a function of desired levels of accuracy and privacy, for both models. We also explicitly construct query strategies whose worst-case query complexity is optimal up to an additive constant.en_US
dc.description.statementofresponsibilityby Zhi Xu.en_US
dc.format.extent108 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.titlePrivate sequential search and optimizationen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1006509772en_US


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