Privacy papers in NeurIPS 2019
Published:
I have curated and am beginning to read NeurIPS ‘19 papers related to privacy. The list will be constantly updated with the paper summaries. Stay tuned!
| Title | Summary |
|---|---|
| Private Hypothesis Selection | Given samples from an unknown probability distribution, select a distribution from some fixed set of candidates which is “close” to the unknown distribution in some appropriate distance measure. |
| Differentially Private Algorithms for Learning Mixtures of Separated Gaussians | Learning the parameters of Gaussian mixture models. sample complexity is small and no a priori bounds on the parameters of the mixture components. |
| Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation | |
| Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection | Show that DP implies generalization. but their concrete examples (lipschitz constraint etc.) did not show how DP is achieved (?). |
| Differentially Private Bayesian Linear Regression | |
| Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases | |
| Locally Private Gaussian Estimation | Each of n users draws a single i.i.d. sample from an unknown Gaussian distribution, and the goal is to estimate the mean of this Gaussian distribution while satisfying local differential privacy for each user. |
| Capacity Bounded Differential Privacy | Limit the capability of adversary, i.e., adversary is capable of performing only linear classification. |
| Practical Differentially Private Top-k Selection with Pay-what-you-get Composition | |
| Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation | |
| Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy | |
| Differentially Private Markov Chain Monte Carlo | |
| Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate | |
| Oblivious Sampling Algorithms for Private Data Analysis | |
| Differentially Private Anonymized Histograms | |
| Facility Location Problem in Differential Privacy Model Revisited | |
| Private Learning Implies Online Learning: An Efficient Reduction | |
| Online Learning via the Differential Privacy Lens | |
| Elliptical Perturbations for Differential Privacy | |
| Limits of Private Learning with Access to Public Data | |
| Private Testing of Distributions via Sample Permutations | |
| Private Stochastic Convex Optimization with Optimal Rates | |
| Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces | |
| Privacy Amplification by Mixing and Diffusion Mechanisms | |
| On Differentially Private Graph Sparsification and Applications | |
| An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors | |
| User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning | |
| Differentially Private Covariance Estimation | |
| Differentially Private Distributed Data Summarization under Covariate Shift | |
| Locally Private Learning without Interaction Requires Separation | |
| Differential Privacy Has Disparate Impact on Model Accuracy | If the original model is unfair, the unfairness becomes worse once DP is applied. |