Algorithmic manipulation of probability distributions for networks and mechanisms

dc.contributor.advisor Peng, Richard
dc.contributor.author Durfee, David
dc.contributor.committeeMember Vempala, Santosh
dc.contributor.committeeMember Chen, Xi
dc.contributor.committeeMember Vigoda, Eric
dc.contributor.committeeMember Toriello, Alejandro
dc.contributor.department Computer Science
dc.date.accessioned 2020-05-20T16:46:57Z
dc.date.available 2020-05-20T16:46:57Z
dc.date.created 2019-05
dc.date.issued 2018-12-19
dc.date.submitted May 2019
dc.date.updated 2020-05-20T16:46:57Z
dc.description.abstract In this thesis we present four different works that solve problems in dynamic graph algorithms, spectral graph algorithms, computational economics, and differential privacy. While these areas are not all strongly correlated, there were similar techniques integral to each of the results. In particular, a key to each result was carefully constructing probability distributions that interact with fast algorithms on networks or mechanisms for economic games and private data output. For the fast algorithms on networks this required utilizing essential graph properties for each network to determine sampling probabilities for sparsification procedures that we often recursively applied to achieve runtime speedups. For mechanisms in economic games we construct a gadget game mechanism by carefully manipulating the expected payoff resulting from the probability distribution on the strategy space to give a correspondence between two economic games and imply a hardness equivalence. For mechanisms on private data output we construct a smoothing framework for input data that allows private output from known mechanisms while still maintaining certain levels of accuracy.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/62623
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Algorithms
dc.subject Sampling
dc.subject Networks
dc.subject Mechanisms
dc.title Algorithmic manipulation of probability distributions for networks and mechanisms
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Peng, Richard
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computer Science
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relation.isOrgUnitOfPublication 6b42174a-e0e1-40e3-a581-47bed0470a1e
thesis.degree.level Doctoral
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