Paper proposes P-ADMM for ADMM in distributed medical machine learning with differential privacy.
problem Privacy leakage in ADMM for distributed machine learning with sensitive data.
method Integrates Gaussian noise with linearly decaying variance to provide dynamic zCDP.
result P-ADMM achieves the same convergence rate as non-private ADMM while ensuring differential privacy.
Paper develops differentially private methods for estimating precision matrices.
problem Estimating precision matrices from sensitive data while maintaining privacy.
method Differential privacy framework, ridge estimator, graphical lasso estimator, ADMM algorithm.
result The proposed methods provide utility in estimating precision matrices from private data.
Novel PP-ADMM and IPP-ADMM algorithms improve differential privacy in distributed machine learning.
problem Privacy concerns in ADMM-based distributed machine learning.
method Proposes PP-ADMM and IPP-ADMM algorithms to provide differential privacy while improving model accuracy and convergence.
result The proposed algorithms achieve better model accuracy and convergence under the same privacy guarantee.
Improved ADMM for convex distributed learning with differential privacy.
problem Privacy concerns in distributed learning with sensitive data.
method Approximate multi-step ADMM with calibrated noise.
result Higher utility and error bounds asymptotic to state-of-the-art.
Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing. Such an iterative process could cause privacy concerns o…
Paper tackles privacy-preserving distributed learning with ADMM.
problem Privacy-preserving distributed learning with vertically partitioned features.
method Proposes ADMM sharing framework to minimize risk over distributed features.
result Demonstrates efficient convergence and privacy guarantees for ADMM algorithms.
Paper proposes ADMM algorithms for non-smooth optimization under RDP.
problem Optimizing composite functions with non-smooth penalties under privacy constraints.
method Developed ssADMM and mpADMM algorithms for non-smooth optimization problems with RDP guarantees.
result Both ssADMM and mpADMM outperform baseline methods in high privacy settings.
Paper develops privacy-preserving federated learning for nonsmooth objectives.
problem Solving nonsmooth objective functions in a privacy-preserving manner.
method Zero-concentrated differential privacy (zCDP) with Gaussian noise, distributed ADMM, and approximation of augmented Lagrangian.
result The algorithm achieves a competitive privacy-accuracy trade-off and converges to the exact solution.
Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed on local data with the output exchanged among neighbors in an iterative fashion. During this iterative process the leakage of data privacy a…
Paper proposes a privacy-preserving DML framework using local randomization and ADMM perturbation.
problem Privacy concerns in distributed machine learning with sensitive user data.
method Local randomization and ADMM perturbation to provide differential privacy and heterogeneous privacy levels.
result The framework minimizes privacy losses and maintains model generalization.
Unified analysis of stochastic ADMM variants via SME.
problem Analyzing and optimizing stochastic ADMM variants for machine learning.
method Unified mathematical framework of SME for continuous-time analysis.
result Dynamics of stochastic ADMM approximated by SDEs with small noise.
Paper presents an ADMM-based approach to efficiently integrate quadratic programming layers into neural networks.
problem Integrating quadratic programs into neural networks for optimization.
method An ADMM-based network layer architecture for solving quadratic programs efficiently.
result The ADMM layer is approximately an order of magnitude faster than existing methods for medium scaled problems.
Paper introduces differential privacy for sparse classification learning.
problem Privacy-preserving sparse classification learning.
method Differential privacy via ADMM with exponential noise addition.
result Proposes a privacy-preserving logistic regression algorithm.
A faster ADMM method for nonconvex optimization with improved complexity.
problem Nonconvex optimization problems in machine learning.
method SPIDER-ADMM, a stochastic ADMM method using a new differential estimator.
result Achieves optimal IFO complexity of O(n+n1/2ε−1) for finding an ε-approximate stationary point. Private learning can be used to efficiently solve online learning problems.
problem The relationship between differentially private learning and online learning efficiency.
method Derive an efficient black-box reduction from differentially private learning to online learning from expert advice.
result An efficient differentially private learner implies an efficient online learner.
Differentially private hyperparameter tuning improves privacy in machine learning.
problem Hyperparameter tuning leaks private information through selected configurations.
method Local Bayesian optimization using Gaussian Process surrogate for private gradient approximation.
result DP-GIBO converges to locally optimal hyperparameters with polynomial dimensional dependence.
Differentially private algorithms for submodular maximization under various constraints.
problem Maximizing decomposable submodular functions under constraints while preserving privacy.
method Designing differentially private algorithms for both monotone and non-monotone decomposable submodular maximization under general matroid constraints.
result Improved utility guarantees and competitive performance compared to non-private algorithms.
Transform non-private e-values into differentially private ones.
problem Leaking sensitive data through non-private e-values.
method Developed a novel biased multiplicative noise mechanism.
result Differentially private e-values maintain strong statistical power and asymptotic equivalence to non-private ones.
Proposes D-LADMM for solving constrained optimization problems.
problem Constrained optimization problems with ill-posed inverse problems.
method Introduces Differentiable Linearized ADMM (D-LADMM) with learnable weights and activation functions.
result Rigorously proves globally converged solutions for D-LADMM.
Optimizes private statistics with noisy methods.
problem Private inference in statistical models.
method Noisy optimization for M-estimators and confidence regions.
result Private estimators converge to non-private ones with high probability.
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.
New algorithms for privately learning decision lists and halfspaces.
problem Private learning of decision lists and halfspaces.
method Differentially private algorithms for PAC and online models.
result Private algorithms match or surpass non-private guarantees.
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.
New private algorithms learn large-margin halfspaces efficiently.
problem Learning large-margin halfspaces with privacy constraints.
method Differentially private algorithms based on a new approach.
result Sample complexity depends only on the margin, not dimension.
The paper proposes differentially private sliced inverse regression algorithms for high-dimensional data.
problem Privacy concerns in high-dimensional data analysis.
method Differentially private sliced inverse regression algorithms designed for privacy preservation.
result Achieves minimax lower bounds up to logarithmic factors.
Optimizes differentially private kernel learning with random projection.
problem Privacy-preserving learning algorithms with optimal performance.
method Differentially private kernel ERM algorithm based on random projection in reproducing kernel Hilbert space.
result Achieves minimax-optimal excess risk rates for various loss functions.
Improved privacy and utility in machine learning with adaptive differential privacy.
problem Enhancing privacy in machine learning models while maintaining utility.
method Adaptive differentially private (ADP) learning method that optimally adapts noise to stepsize.
result ADP method significantly improves utility compared to standard differentially private methods.
DPpack offers R tools for private data analysis and machine learning.
problem Ensuring privacy in statistical analysis and machine learning.
method Differential privacy mechanisms (Laplace, Gaussian, exponential).
result User-friendly implementation of privacy-preserving models.
Differential privacy is a cryptographically-motivated definition of privacy which has gained significant attention over the past few years. Differentially private solutions enforce privacy by adding random noise to a function computed over the data, and the challenge in designing such algorithms is to control the added…
Differentially private random block coordinate descent improves utility in machine learning.
problem Lack of privacy in classical CD methods when handling sensitive information.
method Proposes a differentially private random block coordinate descent method using sketch matrices and importance sampling.
result Demonstrates improved convergence rates and utility guarantees compared to non-private methods.
Develops a computationally tractable differentially private mean estimator called the balloon mean.
problem Robust mean estimation in the presence of outliers and heavy-tailed distributions.
method Iterative clipping procedure over Mahalanobis balls.
result Balloon mean is robust to outliers and outperforms existing estimators in contaminated settings.
Efficient algorithms learn geometric shapes privately with limited data.
problem Learning geometric shapes privately with minimal data.
method Differentially private algorithms for learning unions of polygons.
result Achieves (α,β)-PAC learning and (ε,δ)-differential privacy with a sample size of $ ilde{O}\left(\frac{1}{αε}k\log d
ight)$. Improved differentially private drug sensitivity prediction using compact representations.
problem Challenges in differentially private machine learning with genomic data.
method Representation learning using variational autoencoders, PCA, and random projection.
result Variational autoencoders provide the most accurate predictions for differentially private drug sensitivity prediction.
Improved differentially private deep learning with group-wise clipping techniques.
problem Efficiency and privacy trade-offs in deep learning models.
method Group-wise clipping techniques (per-layer and per-device) to reduce compute time and memory overhead.
result Private learning with group-wise clipping achieves similar or better performance than non-private learning with less wall time.
Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.
problem Developing private tests for simple and MLR hypotheses under Gaussian differential privacy.
method A private mean estimator with data-driven clamping bounds, constructing private test statistics.
result Private tests achieve the same asymptotic relative efficiency as non-private most powerful tests.
Differentially private algorithms protect model explanations from leaking training data.
problem Model explanations can leak training data, compromising privacy.
method Adaptive differentially private gradient descent algorithm to produce accurate, private explanations.
result Privacy amplification and reduction of overall privacy loss on explanation data.
Private learner for halfspaces with efficient sample complexity.
problem Private learning of halfspaces over arbitrary domains.
method Differential privacy for center point location and its application to halfspace learning.
result Private halfspace learning with sample complexity poly(d,2log∗∣X∣). Differentially private ensemble classifiers adapt to data streams while protecting privacy.
problem Adapting to evolving data characteristics while protecting private information.
method Unbounded ensemble updates, model agnostic approach.
result Outperforms competitors on various privacy, drift, and distribution settings.
New method for estimating median and mean with high probability privacy.
problem Estimating median and mean with differential privacy.
method Propose, Test, Release (PTR) mechanism with concentration inequalities.
result First sub-Gaussian high probability bounds for differentially private median and mean estimation.
Private PGB boosts synthetic data quality using GANs and privacy techniques.
problem Differentially private GANs struggle with convergence and poor output quality.
method Combines reweighted samples from GAN training using Private Multiplicative Weights method.
result Improves synthetic data quality across various datasets and tasks.
Private method measures nonlinear correlations between data hosted across two entities.
problem Measuring nonlinear correlations between sensitive data hosted across multiple parties while preserving privacy.
method Differentially private estimator of distance correlation.
result First private estimator of nonlinear correlations in a multi-party setup.
New private algorithms estimate location parameters with sub-Gaussian deviations.
problem Estimating location parameters with differential privacy and sub-Gaussian deviations for heavy-tailed data.
method Design two private algorithms for estimating the median and mean under differential privacy, showing sub-Gaussian deviations for unbounded random variables.
result Private median and mean estimators achieve sub-Gaussian deviations, unlike non-private counterparts which can have strictly worse deviations.
Locally private Gaussian estimation tackles privacy in i.i.d. sample mean estimation.
problem Estimating the mean of an unknown Gaussian distribution while maintaining local differential privacy for each user.
method Adaptive two-round and nonadaptive one-round solutions for locally private Gaussian estimation.
result Upper bounds partially match with information-theoretic lower bounds, showing tightness up to logarithmic factors.
This paper introduces differentially private permutation tests for hypothesis testing.
problem Privacy concerns in sensitive data analysis.
method Differentially private permutation tests for kernel methods.
result Proposes dpMMD and dpHSIC for two-sample and independence testing, achieving optimal power.
PriMORL trains private RL policies on offline data.
problem Private reinforcement learning on offline data.
method PriMORL learns DP models of the environment and optimizes a policy on the penalized private model.
result PriMORL enables training of private RL agents on complex tasks.
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.
DiPriMe forests use private medians to create balanced tree splits for privacy-protected data.
problem Privacy concerns in training random forests due to multiple data queries.
method Proposes DiPriMe forests, which use a private median to generate balanced splits, ensuring differential privacy.
result DiPriMe forests achieve high utility while maintaining differential privacy, as shown both theoretically and empirically.
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…