Private estimation with public data reduces sample complexity.
problem Estimating private distributions with limited public data.
method Differentially private estimation with public data under constraints of pure or concentrated DP.
result Public data can significantly reduce private sample complexity for estimation.
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.
Private distribution learning with public data, leveraging sample compression schemes.
problem Private distribution learning with public and private samples under differential privacy constraints.
method Connection to sample compression schemes and list learning.
result At least d public samples are necessary for private learnability of Gaussians in R^d.
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.
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.
Efficiently learns private models using public data.
problem Improving private learning performance with public data.
method Proves computationally efficient algorithms for private learning with public data.
result First computationally efficient algorithms for private learning with public data.
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…
Algorithm selects public datasets for private machine learning.
problem Choosing the most suitable public dataset for private machine learning.
method Measures gradient subspace distance between public and private datasets.
result Excess risk scales with the subspace distance between gradients.
Study private query release with public data, reducing sample sizes.
problem Answering a wide range of statistical queries while maintaining privacy.
method Combines public and private samples to answer queries with differential privacy.
result Private and public sample complexities for different query classes.
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.
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.
New method uses public data to achieve optimal nonparametric classification with privacy constraints.
problem Optimal nonparametric classification under local differential privacy constraints.
method Locally differentially private classification tree with data-driven pruning.
result Achieves mini-max optimal convergence rate with public data.
New private learning algorithms improve utility in tasks with public features.
problem Private learning with public features in recommendation and ad prediction.
method Developed algorithms that protect only certain sufficient statistics, improving utility for linear regression and private recommendation benchmarks.
result Achieved state-of-the-art performance on private recommendation benchmarks.
Publicly pretraining models on Web data may undermine differential privacy.
problem The use of large Web-scraped datasets in differential privacy models.
method Critical review of leveraging pretrained models on public datasets for differential privacy.
result Publicizing pretrained models as 'private' could harm trust and generalize poorly.
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.
Combines public and private data for better statistical estimation.
problem Estimating aggregate statistics from mixed data with varying privacy needs.
method Mixed estimators optimized for minimizing variance or median, using differential privacy techniques.
result Our mechanisms often outperform baseline methods in empirical tests.
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.
Optimal DP model training with public data improves privacy and accuracy.
problem Ensuring privacy while training models with public data.
method Proves optimal error rates for DP model training with public data, develops novel algorithms.
result Optimal error rates can be achieved by using public data or optimal DP algorithms.
This paper investigates the relationship between private and public investment in R&D, while taking into account the effect of several instruments policies such as subsidies and taxes. We design a new look of knowledge spillovers and R&D cooperation to explain the contribution of public and private R&D on growth. We pr…
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.
Enhances DP linear regression using public data moments.
problem Limited utility of traditional DP methods in linear regression.
method Transform private data using public second-moment matrix for a better OLSE.
result Improved accuracy and robustness of OLSE in DP linear regression.
PMT uses public data moments to make DP feasible for unbounded data.
problem Applying differential privacy to unbounded data distributions.
method Public-moment-guided Truncation (PMT) using second-moments from public data.
result PMT improves the accuracy and stability of DP models.
Study of public and private VC relationships in France using qualitative methods.
problem Understanding interactions between public and private venture capitalists in France.
method Qualitative approach with semi-structured interviews and thematic content analysis.
result Formal or informal relationships between public and private VCs are a 'economico-cognitive' approach to networking and innovation.
Private equity deals predict public market returns with up to 70% accuracy.
problem Predicting public market behavior from private equity transactions.
method Logit model using detailed analysis of private equity diligence process.
result Model predicts public market returns with up to 70% accuracy in specific sectors.
Improved image generation with private data using perceptual features.
problem Difficulty in training generative models with differential privacy.
method Use pre-trained perceptual features to learn private data distribution.
result Generative models can generate high-quality images with low privacy budget (ϵ≈2). Study on InstaHide's security, linking to phase retrieval problem.
problem Security of InstaHide scheme for private dataset sharing.
method Design of a provable algorithm for private vector recovery.
result Private vectors can be recovered using synthetic vectors and public vectors.
New DP EM algorithm with statistical guarantees for mixture models.
problem Preserving privacy in EM algorithms for mixture models.
method Proposed a DP EM algorithm with statistical guarantees.
result Near optimal estimation error for GMM in DP model.
Private learning of Gaussian Mixture Models without boundedness assumptions.
problem Private estimation of parameters of Gaussian Mixture Models with unbounded components.
method Reduction to non-private problem, blackbox privatization, Moitra and Valiant's algorithm.
result First sample complexity upper bound and polynomial time algorithm for privately learning GMMs.
Private learning needs more data or better features.
problem Improving differentially private machine learning performance.
method Demonstrates the need for either more private data or better features.
result Private learning requires either more data or better features.
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.
Efficient private algorithms for estimating block models and mixture models.
problem Estimating block models and mixture models in high-dimensional settings.
method General tools for designing efficient private estimation algorithms.
result First efficient private algorithms for weak and exact recovery of stochastic block models.
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
problem Balancing public benefits and private insurance for loss of earning capacity in Denmark.
method Innovative approaches from researchers and practitioners, leveraging actuarial expertise.
result Development of equitable, data-driven solutions to mitigate risk and enhance societal well-being.
This paper relaxes the common prior assumption in the public and private information game of Morris and Shin (2000, 2004). For the generalized game, where the agent's prior expectations are heterogenous, it derives a sharp condition for the emergence of unique/multiple equilibria. This condition indicates that unique e…
Study on markets with insiders receiving private signals affecting asset prices and information flow.
problem Understanding markets with heterogeneous information flows and private signals.
method Proves existence of a partial communication equilibrium with jumps in information and prices.
result The public information flow and asset prices jump at each private signal time, creating incomplete markets between jumps.
This paper analyzes the relationship between public disclosure, private information and stock liquidity in Tunisian context using a sample of 41 listed firms in the Tunis Stock Exchange in 2007. First, we find no evidence that there is a relation between public and private information. Second, Tunisian investors do not…
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.
Improved sample efficiency for private learning of Gaussian mixtures.
problem Learning mixtures of Gaussians with differential privacy.
method Inverse sensitivity mechanism, sample compression, sumset volume bounds.
result Proved optimal sample complexity for private learning of mixtures of Gaussians.
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…
Within the Private Equity (PE) market, the event of a private company undertaking an Initial Public Offering (IPO) is usually a very high-return one for the investors in the company. For this reason, an effective predictive model for the IPO event is considered as a valuable tool in the PE market, an endeavor in which …
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the parameters of a high-dimensional, well separated, Gaussian mixture model subject to the strong constraint of differential privacy. In particula…
Study shows private learning of mixtures of Gaussians is possible with polynomial samples.
problem Estimating mixtures of Gaussians under differential privacy constraints.
method Developed a new framework for privately learning mixtures of Gaussians without structural assumptions.
result Polynomial number of samples (poly(k,d,1/α,1/ε,log(1/δ))) sufficient for estimation up to total variation distance α with (ε, δ)-DP.
Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a…
We propose a novel framework for the differentially private ERM, input perturbation. Existing differentially private ERM implicitly assumed that the data contributors submit their private data to a database expecting that the database invokes a differentially private mechanism for publication of the learned model. In i…
DP-RandP improves privacy-utility tradeoff in DP-SGD by learning priors from random processes.
problem Improving the performance of differentially private stochastic gradient descent (DP-SGD) on private data.
method A three-phase approach that learns priors from images generated by random processes and transfers these priors to private data.
result New state-of-the-art accuracy on CIFAR10, CIFAR100, MedMNIST, and ImageNet for various privacy budgets.
Differentially private learning avoids iterative optimization in parameter space.
problem Differentially private training of neural networks
method Hypernetworks trained on public datasets
result Significantly reduces noise in parameter space
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.
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
problem Tedium and subjectivity in traditional cost of equity and comparable company analysis methods.
method Uses spectral and agglomerative clustering to compute cost of equity and perform comparable company analysis.
result Reduces time required for comps by orders of magnitude and improves consistency and reliability.
Study private submodular maximization in streaming data.
problem Private maximization of submodular functions in streaming data.
method Established differentially private baselines and derived better trade-offs for decomposable submodular functions.
result Improved trade-offs between privacy and utility for decomposable submodular functions.