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Transfer Learning In Differential Privacy's Hybrid-Model
The hybrid-model (Avent et al 2017) in Differential Privacy is a an augmentation of the local-model where in addition to N local-agents we are assisted by one special agent who is in fact a curator holding the sensitive details of n additional individuals.
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme
First, we propose a (non-private) successive elimination algorithm for strictly optimal best-arm identification, we show that our algorithm is $\delta$-PAC and we characterize its sample complexity.
The power of synergy in differential privacy: Combining a small curator with local randomizers
Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al.,\ USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users.
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications.
An Optimal Private Stochastic-MAB Algorithm Based on an Optimal Private Stopping Rule
We present a provably optimal differentially private algorithm 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 $\Omega \left(\frac{K\log(T)}{\epsilon} \right)$ [Shariff and Sheffet, 2018].
Differentially Private Contextual Linear Bandits
We study the contextual linear bandit problem, a version of the standard stochastic multi-armed bandit (MAB) problem where a learner sequentially selects actions to maximize a reward which depends also on a user provided per-round context.
Locally Private Hypothesis Testing
Under the mechanism of Bassily et al we give identity and independence testers with better sample complexity than the testers in the symmetric case, and we also propose a $\chi^2$-based identity tester which we investigate empirically.
Differentially Private Ordinary Least Squares
Linear regression is one of the most prevalent techniques in machine learning, however, it is also common to use linear regression for its \emph{explanatory} capabilities rather than label prediction.
Learning Mixtures of Ranking Models
We present the first polynomial time algorithm which provably learns the parameters of a mixture of two Mallows models.
Differentially Private Data Analysis of Social Networks via Restricted Sensitivity
Specifically, given a query f and a hypothesis H about the structure of a dataset D, we show generically how to transform f into a new query f_H whose global sensitivity (over all datasets including those that do not satisfy H) matches the restricted sensitivity of the query f. Moreover, if the belief of the querier is correct (i. e., D is in H) then f_H(D) = f(D).
Cryptography and Security Social and Information Networks Physics and Society
