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Skip to Search Results- 4Differential Privacy
- 1Bayesian Inference
- 1Bayesian hierarchical modeling
- 1Data Synthesis
- 1Differentially Private Stochastic MAB
- 1Differentially Private Stopping Rule
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Fall 2023
This thesis presents a comprehensive study of Gaussian Differential Privacy (GDP) and Local Differential Privacy (LDP), exploring their properties, relationships, and applications in developing novel algorithms and optimization methods for efficient and accurate privacy-preserving data analysis....
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Bayesian hierarchical modeling and its applications to clustering and data privacy preservation
DownloadFall 2024
The evolution of data acquisition technologies and the exponential growth in computing capabilities have inaugurated an epoch wherein researchers are empowered to procure data of unprecedented dimensionality and complexity. Simultaneously, Bayesian hierarchical models distinguish themselves as...
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Fall 2021
The demand for large dataset and demand of privacy protection are in constantly conflicts as the balance between the two is hard to keep. Differential privacy is a mathematical rigor definition that provides the balance bewteen these two opposite sides. It's developed with the purpose of making...
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Fall 2019
We present two provably optimal differentially private algorithms for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm (Mishra and Thakurta 2015; Tossou and Dimitrakakis 2016) which doesn’t meet the recently discovered lower-bound of Ω( K log(T) /...