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Found 22 papers, 2 papers with code
Private PAC Learning May be Harder than Online Learning
We continue the study of the computational complexity of differentially private PAC learning and how it is situated within the foundations of machine learning.
Oracle-Efficient Differentially Private Learning with Public Data
Due to statistical lower bounds on the learnability of many function classes under privacy constraints, there has been recent interest in leveraging public data to improve the performance of private learning algorithms.
Not All Learnable Distribution Classes are Privately Learnable
We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not learnable under $(\varepsilon, \delta)$-differential privacy.
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
In particular, we give sample-efficient algorithmic reductions between perfect generalization, approximate differential privacy, and replicability for a broad class of statistical problems.
Strong Memory Lower Bounds for Learning Natural Models
We give lower bounds on the amount of memory required by one-pass streaming algorithms for solving several natural learning problems.
Multiclass versus Binary Differentially Private PAC Learning
We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning.
Differentially Private Correlation Clustering
Correlation clustering is a widely used technique in unsupervised machine learning.
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Our problems are simple and fairly natural variants of the next-symbol prediction and the cluster labeling tasks.
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design.
A Computational Separation between Private Learning and Online Learning
A recent line of work has shown a qualitative equivalence between differentially private PAC learning and online learning: A concept class is privately learnable if and only if it is online learnable with a finite mistake bound.
New Oracle-Efficient Algorithms for Private Synthetic Data Release
We present three new algorithms for constructing differentially private synthetic data---a sanitized version of a sensitive dataset that approximately preserves the answers to a large collection of statistical queries.
An Equivalence Between Private Classification and Online Prediction
We prove that every concept class with finite Littlestone dimension can be learned by an (approximate) differentially-private algorithm.
Efficient, Noise-Tolerant, and Private Learning via Boosting
To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspaces whose sample complexity does not depend on the dimension.
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
The simplest and most widely applied method for guaranteeing differential privacy is to add instance-independent noise to a statistic of interest that is scaled to its global sensitivity.
Statistics Theory Cryptography and Security Data Structures and Algorithms Statistics Theory
Private Hypothesis Selection
The sample complexity of our basic algorithm is $O\left(\frac{\log m}{\alpha^2} + \frac{\log m}{\alpha \varepsilon}\right)$, representing a minimal cost for privacy when compared to the non-private algorithm.
Differentially Private Submodular Maximization: Data Summarization in Disguise
Many data summarization applications are captured by the general framework of submodular maximization.
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations.
Make Up Your Mind: The Price of Online Queries in Differential Privacy
The queries may be chosen adversarially from a larger set Q of allowable queries in one of three ways, which we list in order from easiest to hardest to answer: Offline: The queries are chosen all at once and the differentially private mechanism answers the queries in a single batch.
Simultaneous Private Learning of Multiple Concepts
We investigate the direct-sum problem in the context of differentially private PAC learning: What is the sample complexity of solving $k$ learning tasks simultaneously under differential privacy, and how does this cost compare to that of solving $k$ learning tasks without privacy?
Order-Revealing Encryption and the Hardness of Private Learning
An order-revealing encryption scheme gives a public procedure by which two ciphertexts can be compared to reveal the ordering of their underlying plaintexts.
Differentially Private Release and Learning of Threshold Functions
Our sample complexity upper and lower bounds also apply to the tasks of learning distributions with respect to Kolmogorov distance and of properly PAC learning thresholds with differential privacy.
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness
The power of this algorithm relies on the fact that under log-concave distributions, halfspaces can be approximated arbitrarily well by low-degree polynomials.
