Study on federated learning with private label sets, showing privacy benefits without significant accuracy loss.
problem Effects of label set heterogeneity and privacy constraints in federated learning.
method Apply classical classifier combination methods and adapt FL methods for private label sets, compare public and private settings.
result Reducing labels harms model performance, but centralized tuning can help.
We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and cl…
We present heuristics for solving the maximin problem induced by the generative adversarial privacy setting for linear and convolutional neural network (CNN) adversaries. In the linear adversary setting, we present a greedy algorithm for approximating the optimal solution for the privatizer, which performs better as th…
PILLAR improves SP learning with less private data.
problem Efficiently learning with semi-private data under privacy constraints.
method Uses pre-trained public data features to reduce private data requirements.
result Significantly lower private labelled sample complexity achieved.
Differentially-private Bayes consistency rule for binary classification and density estimation.
problem Privacy constraints limit private learning in the distribution-free PAC model.
method Constructs a universally Bayes consistent learning rule that satisfies differential privacy.
result Private learning is possible for arbitrary distributions, even with a single algorithm.
Private algorithms adapt from public to private domains with minimal labeled data.
problem Adapting from a public source domain to a private target domain with few labeled data.
method Differentially private discrepancy minimization algorithms based on Frank-Wolfe and Mirror-Descent methods.
result Effective adaptation with strong generalization and privacy guarantees.
Study public-data assisted private stochastic optimization with labeled or unlabeled public data.
problem Limits and capability of public-data assisted differentially private (PA-DP) algorithms in stochastic convex optimization.
method Lower bounds for PA-DP mean estimation and novel methods for leveraging public data in private supervised learning.
result Achieved dimension independent rate for GLM with unlabeled public data, showing optimality.
DP-CGAN generates private synthetic data and labels.
problem Preserving privacy in synthetic data generation.
method Differentially private conditional GAN (DP-CGAN) with clipping and perturbation.
result DP-CGAN generates visually and empirically promising results on MNIST with low privacy cost.
Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretica…
We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization~[Blum et al. 2008] under pure ε-differential privacy [Dwork et al. TCC 2006] and approximate (ε,δ)-differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approxima…
It is challenging for stochastic optimizations to handle large-scale sensitive data safely. Recently, Duchi et al. proposed private sampling strategy to solve privacy leakage in stochastic optimizations. However, this strategy leads to robustness degeneration, since this strategy is equal to the noise injection on each…
Improved DLG extracts accurate labels from gradients, overcoming DLG's convergence issues.
problem Private training data leakage from shared gradients in distributed learning systems.
method Proposes iDLG, a simple approach to synthesize accurate labels from gradients.
result iDLG reliably extracts ground-truth labels from gradients, unlike DLG.
Two private algorithms improve domain adaptation with privacy guarantees.
problem Improving predictions for a private target domain using public data.
method Two (ε,δ)-differentially private algorithms for supervised domain adaptation. result Private algorithms maintain performance close to non-private versions.
Discond-VAE separates continuous and discrete factors in data.
problem Separating shared and class-specific variations in real-world data.
method Introduces private and public latent variables to represent continuous and discrete factors, respectively.
result Discond-VAE successfully disentangles class-dependent continuous factors from discrete factors.
Improved private agnostic learning with near-optimal sample complexity.
problem Private agnostic learning with arbitrary privacy parameters.
method Near-optimal sample complexity construction.
result Near-optimal extra sample complexity of \(\widetilde{O}(\mathrm{VC}(\mathcal{C})/α^2)\) for any \(\varepsilon \leq 1\).
Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data. We wish to reduce the correlation between user-specific private information…
Generative models help debug data issues in private, decentralized datasets.
problem Manual data inspection is impractical for private, decentralized datasets.
method Differentially private federated generative models.
result Generative models can effectively debug data issues in private datasets.
Framework for private, noise-tolerant, and efficient learning algorithms.
problem Private and efficient learning of large-margin halfspaces in noisy environments.
method Simple framework using differential privacy and noise tolerance conditions.
result Noise-tolerant and private PAC learners for large-margin halfspaces with sample complexity independent of dimension.
Bounds on Littlestone dimension for private learning and online prediction.
problem Understanding the Littlestone dimension of composed classes for private learning.
method Deriving bounds on Littlestone dimension and transforming private learners.
result Improved bounds on sample complexity for private learning.
Few-shot DP image classification models need more data as privacy increases.
problem Few-shot DP image classification challenges in personalization and federated learning.
method Exhaustive experiments on various parameters affecting few-shot DP accuracy and vulnerability.
result Increasing shots per class is necessary for DP accuracy as privacy increases.
New privacy-preserving learning model for mixtures of private and public data.
problem Learning from datasets with both private and public data, where privacy concerns differ.
method Designing a differential privacy-preserving learning algorithm for a mixture of private and public sub-populations.
result Linear classifiers can be learned with sample complexity comparable to non-private PAC-learning, even when privacy status correlates with labels.
Polynomial-time algorithm for near-optimal community detection in graphs.
problem Node-private community estimation in stochastic block models.
method Explicit Lipschitz surrogate and accept-reject algorithm for sampling community labels.
result Achieves minimax rates for exact recovery with polynomial-time runtime and logarithmic privacy parameter.
Differentially private graph learning via bounded sensitivity PPR.
problem Protecting user data in graph learning algorithms.
method Proposes a sensitivity-bounded personalized PageRank (PPR) algorithm.
result Achieves similar accuracy to non-private algorithms with large degrees.
New method for private learning with public features improves convergence rates.
problem Private estimation with public features under local differential privacy.
method Semi-feature LDP, HistOfTree estimator.
result HistOfTree reaches mini-max optimal convergence rate.
A key factor in developing high performing machine learning models is the availability of sufficiently large datasets. This work is motivated by applications arising in Software as a Service (SaaS) companies where there exist numerous similar yet disjoint datasets from multiple client companies. To overcome the challen…
This paper studies trade-offs in private prediction methods.
problem Leakage of training data information in machine learning predictions.
method Private training and private prediction methods with trade-offs.
result Private training methods outperform private prediction methods in various settings.
Paper optimizes private ReLU regression with relaxed assumptions.
problem Private ReLU regression with relaxed assumptions.
method One-pass mini-batch Generalized Linear Model Perceptron algorithm (DP-MBGLMtron).
result Achieves optimal utility bound up to logarithmic factors.
Improves node classification in graphs with active learning.
problem Difficult or expensive labeling in node classification tasks.
method Graph cognizant logistic regression and preemptive query generation.
result Significant improvement over state-of-the-art approaches.
Public pretraining improves private model training even in extreme distribution shift scenarios.
problem Improving private model training accuracy in settings with large distribution shift.
method Empirical evaluation and theoretical explanation of public representations improving private training accuracy.
result Public representations can improve private training accuracy by up to 67% over private training from scratch in settings with large distribution shift.
Proposes IFCDA framework to improve cross-domain adaptation.
problem Negative transfer and difficulty in handling category-irrelevant losses in DA.
method Importance filtered mechanism to generate filtered soft labels, combined with graph-based label propagation.
result Significantly improves performance in both Closed-Set and Open-Set DA scenarios.
New method for private density estimation of high-dimensional Gaussian mixtures.
problem Private density estimation for mixtures of unrestricted high-dimensional Gaussians.
method Exploits list global stability to prove upper bound on sample complexity.
result First upper bound on sample complexity for agnostic private density estimation.
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
problem Domain adaptation challenges in heterogeneous networks with shared and private node types.
method Generalized Domain Adaptive model across HINs (GDA-HIN) that aligns identical-type nodes and edges while utilizing different-type nodes and edges.
result GDA-HIN outperforms state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.
Representation learning, especially which by using deep learning, has been widely applied in classification. However, how to use limited size of labeled data to achieve good classification performance with deep neural network, and how can the learned features further improve classification remain indefinite. In this pa…
Private algorithms approximate matrices with private data.
problem Approximate matrices with same spectrum using private data.
method Differential privacy algorithms for unitary orbit optimization.
result Upper and lower bounds on approximation error.
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
problem Understanding the role of credit supply in the U.S. business cycle.
method Causal evidence from 1999-2010 U.S. business cycle data.
result Credit expansion, particularly in private-label mortgages, caused stronger household leverage cycles.
This work improves federated learning privacy and accuracy with non-private data sharing and approximate gradient coding.
problem Challenges of non-IID data and stragglers in federated learning.
method Data-driven strategy combining offline data sharing and approximate gradient coding.
result Achieves a trade-off between privacy and utility, leading to improved model convergence and accuracy.
A framework for private prediction sets using conformal prediction and differential privacy.
problem Jointly addressing reliability and privacy in machine learning predictions.
method Split conformal prediction with privatized quantile subroutine.
result Private prediction sets can be generated from privately-trained models.
This work proves DP learnability implies online learnability for general classification tasks.
problem Link between differential privacy and online learning for general classification tasks.
method Establishes Ramsey-type theorems for trees to prove DP learnability implies online learnability.
result DP learnability implies online learnability for general classification tasks.
Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to …
New method for semi-supervised learning in federated learning with and without labels at clients.
problem Training federated learning models with partially or completely unlabeled data.
method Federated Matching (FedMatch) with inter-client consistency loss and disjoint learning.
result FedMatch outperforms local semi-supervised learning and naive federated learning combinations.
Differentially private conformal prediction improves statistical efficiency.
problem Quantifying uncertainty in private data analysis.
method Introducing differential conformal prediction and developing Differentially Private Conformal Prediction (DPCP).
result DPCP produces tighter prediction sets than existing private split conformal approaches.
Private release of sensitive data enables fair learning.
problem Learning fair predictors with restricted sensitive data.
method Private release of sensitive demographic data, adapting non-discriminatory learners.
result The approach provides theoretical guarantees on performance for fair predictors.
New DP algorithms with margin guarantees for various hypothesis sets.
problem Differential privacy in machine learning with margin guarantees.
method Developed pure and efficient DP learning algorithms for linear, kernel-based, and neural network hypotheses.
result Margin guarantees are independent of input dimension and hypothesis type.
Paper introduces algorithms for private decision tree learning.
problem Private decision tree learning in distributed settings.
method Proposes DP-TopDown, NoisyCounts, and LocalRNM.
result First utility guarantees for private decision tree learning.
Efficiently private regression for unbounded data.
problem Privacy constraints in regression settings with unbounded covariates.
method Differential privacy techniques on mean and covariance estimation extended to sub-gaussian regime.
result Unbiased estimate of true regression vector learned up to a scaling factor.
We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC …
Dynamic model considers private asset markets' complexities.
problem Understanding and optimizing private asset allocation.
method State-of-the-art dynamic model with machine learning.
result Optimal investment policies quantified over fund life.
Improves zeroth-order optimization for private machine learning with public data.
problem High computation and memory cost of first-order DP methods.
method PAZO (Public Data Assisted Zeroth-order Optimization) framework.
result Achieves superior privacy/utility tradeoffs across tasks.