Paper studies optimal federated learning for nonparametric regression with privacy constraints.
problem Federated learning for nonparametric regression with heterogeneous differential privacy constraints.
method Proposes distributed privacy-preserving estimators and investigates their risk properties.
result Establishes matching minimax lower bounds for global and pointwise estimation.
Algorithm ensures privacy while strictly adhering to constraints.
problem Differential privacy with linear constraints that must be strictly followed.
method Developed an algorithm that releases a nearly-optimal solution satisfying constraints with probability 1.
result Achieved nearly optimal performance while preserving privacy and strictly adhering to constraints.
Paper tackles nonparametric classification with privacy constraints, achieving optimal accuracy.
problem Nonparametric classification under distributed differential privacy constraints.
method Minimax and adaptive transfer learning, considering privacy, sample sizes, and heterogeneity.
result Developed an adaptive classifier achieving optimal misclassification rate with privacy constraints.
Quantum channels' contraction under privacy constraints studied.
problem Understanding the privacy constraints on quantum channel contractions.
method Established upper bounds on contraction coefficients for specific divergences under QLDP constraints.
result Upper bounds and full characterization of contraction coefficients for specific quantum distances.
New policy minimizes error in finding best arm with privacy constraints.
problem Best arm identification with differential privacy constraints.
method Maximum absolute determinants principle for ε \varepsilon ε -DP policy. result Error probability decays exponentially in T T T with matching exponents. Paper addresses privacy and communication in distributed learning, achieving optimal performance.
problem Balancing privacy, communication, and accuracy in distributed learning and estimation.
method Developed novel encoding and decoding mechanisms for mean and frequency estimation under local differential privacy and communication constraints.
result Achieved optimal privacy and communication efficiency in mean and frequency estimation.
Study binary hypothesis testing with privacy and communication constraints.
problem Binary hypothesis testing under local differential privacy and communication constraints.
method Qualifies results as minimax or instance optimal, develops instance-optimal algorithms.
result Achieves minimum possible sample complexity under both privacy and communication constraints.
This work optimizes mean estimation under varying privacy constraints.
problem Mean estimation with heterogeneous privacy constraints.
method Proposes an algorithm for mean estimation under different privacy levels for users.
result Shows a saturation phenomenon in performance as privacy levels are relaxed.
We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy. Our learning goals include both structure learning, where we try to estimate the underlying graph structure of the model, as well as the harder goal of parameter l…
Framework for robust decision making in changing environments with privacy constraints.
problem Interactive decision making in changing environments with constraints.
method Hybrid Decision Making with Structured Observations (hybrid DMSO) framework, local differentially private decision making, query-based learning, robust and smooth decision making.
result Strong connections and bounds derived for DEC, SQ dimension, local minimax complexity, learnability, and joint differential privacy.
Improved privacy-preserving statistical estimates with customizable noise reduction.
problem Balancing privacy and accuracy in statistical estimation.
method Introducing the Brownian mechanism, which adds Gaussian noise to a sequence of estimates, gradually reducing it based on the practitioner's needs.
result The Brownian mechanism produces more accurate estimates while maintaining strong privacy guarantees, outperforming existing methods.
Improved sampling from Gaussian distributions with privacy constraints.
problem Sampling from unbounded Gaussian distributions with differential privacy.
method First $\widetilde{\mathcal{O}}\left(d
ight)$ -sample algorithm for unbounded Gaussians under $\left(\varepsilon, δ
ight)$ -differential privacy.
result A quadratic improvement over previous results, settling an open question.
New method for private learning with fairness constraints.
problem Rate-constrained optimization under differential privacy.
method RaCO-DP, a DP variant of SGDA solving Lagrangian formulation.
result Empirical results show RaCO-DP outperforms existing methods.
Data processing inequalities link Fisher information to local differential privacy constraints.
problem Understanding how Fisher information scales with local differential privacy constraints.
method Developed data processing inequalities for Fisher information under local differential privacy.
result Implications for private estimation with optimal bounds and error rates.
New methods test discrete distributions faster with local privacy constraints.
problem Testing discrete distributions under local differential privacy constraints.
method Efficient randomized algorithms and test procedures, both non-interactive and interactive.
result Faster separation rates in interactive privacy mechanisms.
DePAint solves MARL for agents with local constraints, privacy, and no central controller.
problem Training multi-agent systems to optimize rewards while adhering to safety constraints in a decentralized setting.
method Formulated as a decentralized constrained multi-agent Markov Decision Problem, proposed DePAint method using momentum-based decentralized policy gradient.
result First privacy-preserving fully decentralized MARL algorithm considering both peak and average constraints.
New method estimates discrete distributions while protecting privacy.
problem Estimating discrete distributions with local differential privacy.
method Combining robust learning and local differential privacy.
result Minimax estimation rate of ε d / α 2 k + d 2 / α 2 k n ε\sqrt{d/α^2 k}+\sqrt{d^2/α^2 kn} ε d / α 2 k + d 2 / α 2 k n under privacy constraint. New DP methods for estimating means and frequencies with varying privacy demands.
problem Estimating statistics with users having different privacy requirements.
method Proposes algorithms for empirical mean and frequency estimation under heterogeneous privacy constraints, considering both correlated and permuted datasets.
result Establishes theoretical performance guarantees for algorithms, achieving minimax optimality.
New method generates diverse EHR data types while maintaining privacy.
problem Privacy risks in sharing EHRs across large scales.
method Refined GAN model, feature constraints, and utility measures.
result New model preserves EHR statistics, correlations, and constraints.
Paper optimizes federated PCA for covariance estimation under privacy constraints.
problem Privacy-preserving covariance estimation in federated learning.
method Federated PCA, matrix version of van Trees' inequality, three-layer spectral decomposition.
result Optimal rates of convergence for central server's estimation, robust to inconsistent local estimators.
Study differential privacy in multi-agent RL, achieving efficient and private learning.
problem Protecting sensitive data in multi-agent reinforcement learning.
method Extending DP definitions to two-player games, designing an efficient algorithm with privatized bonuses.
result Achieved trajectory-wise differential privacy in multi-agent RL, improving regret bounds.
Paper discusses binary classification with metric space predictors, privacy constraints, and convergence rates.
problem Binary classification with metric space predictors under privacy constraints.
method Derives convergence rates for Proto-NN classifier with and without privacy constraints.
result Proto-NN classifier is universally consistent under privacy constraints.
A DP method selects best sparse models in high dimensions efficiently.
problem Model selection in high-dimensional sparse linear regression under privacy constraints.
method Differential privacy (DP) with exponential mechanism and Metropolis-Hastings algorithm.
result The method identifies active features quickly under privacy constraints.
A privacy-constrained information extraction problem is considered where for a pair of correlated discrete random variables ( X , Y ) (X,Y) ( X , Y ) governed by a given joint distribution, an agent observes Y Y Y and wants to convey to a potentially public user as much information about Y Y Y as possible without compromising the amount of …
Finding anonymization mechanisms to protect personal data is at the heart of recent machine learning research. Here, we consider the consequences of local differential privacy constraints on goodness-of-fit testing, i.e. the statistical problem assessing whether sample points are generated from a fixed density f 0 f_0 f 0 , o…
Framework improves classifier calibration under differential privacy for domain shift.
problem Improving classifier calibration under domain shift with privacy constraints.
method Differential privacy framework for adapting recalibration algorithms.
result Novel accuracy temperature scaling algorithm outperforms existing methods on private datasets.
This work improves privacy in federated combinatorial bandits by balancing regret and privacy.
problem Privacy-preserving learning in competitive online learning settings with quality constraints.
method Proposes P-FCB algorithm for federated combinatorial bandits, balancing regret and privacy.
result Improves regret while maintaining quality constraints and privacy guarantees.
New method for private linear regression under privacy constraints, achieving optimal rates.
problem Statistical complexity of private linear regression under unknown, ill-conditioned covariates.
method Information-Weighted Regression method
result Optimal convergence rates for both central and local privacy models.
DP-SPRT improves privacy in sequential tests with near-optimal error rates.
problem Privacy constraints in sequential probability ratio tests.
method A wrapper for SPRT that uses a private mechanism to determine when to stop based on predefined intervals.
result DP-SPRT achieves near-optimal error rates and privacy guarantees.
Bayesian VFLMSP improves multimodal survival prediction with privacy.
problem Privacy and reliability in multimodal time-to-event prediction.
method Bayesian Vertical Federated Learning (VFL) with differential privacy.
result Consistent improvements in C-index compared to existing methods.
A privacy-preserving algorithm for high-dimensional bandits.
problem High-dimensional stochastic contextual linear bandits with sparse parameters under privacy constraints.
method PrivateLASSO algorithm based on sparse hard-thresholding and episodic thresholding.
result Minimax private lower bounds and utility guarantees for PrivateLASSO.
Simpler, faster algorithm for uniformity testing in the shuffle model.
problem Testing uniformity of data in the shuffle model with privacy constraints.
method Simplified analysis and use of privacy amplification via shuffling.
result An algorithm with the same guarantees but simpler and more streamlined.
Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the …
Optimal privacy and accuracy in distributed mean estimation with compression.
problem Achieving optimal accuracy under privacy and communication constraints.
method Compression to reduce communication while maintaining privacy and accuracy.
result Achieves optimal error with significantly reduced communication.
Paper studies federated nonparametric testing with privacy constraints, achieving optimal rates and adaptive testing.
problem Federated nonparametric goodness-of-fit testing under distributed differential privacy constraints.
method Establishes matching lower and upper bounds on minimax separation rate, constructs adaptive testing procedure.
result Achieves optimal rates and demonstrates phase transition phenomena in federated testing.
Private minimum Hellinger distance estimators maintain robustness and efficiency while ensuring privacy.
problem Ensuring privacy in robust statistical estimation.
method Derive private minimum Hellinger distance estimators satisfying Hellinger differential privacy.
result Private minimum Hellinger distance estimators retain robustness and efficiency under privacy constraints.
New findings show privacy affects generalization error in a non-monotonic way.
problem Privacy and robustness in distributed learning.
method Theoretical analysis and matching lower/upper bounds on algorithmic stability.
result Generalization error is non-monotonically affected by privacy, depending on noise level.
LDP is equivalent to contraction of E_γ-divergence, impacting privacy and utility.
problem Analyzing trade-offs between privacy and utility in estimation problems.
method Equivalence of LDP constraints to contraction coefficients of E_γ-divergence, using f-divergences and estimation-theoretic tools.
result LDP guarantees can be expressed in terms of contraction coefficients of arbitrary f-divergences.
Paper proposes a privacy-preserving knockoff inference method.
problem Ensuring privacy in model-X knockoff inference.
method Differential privacy framework for knockoff inference.
result Guaranteed FDR control with privacy protection.
Paper develops a privacy-preserving nonparametric regression method.
problem Nonparametric regression with local differential privacy constraints.
method Privatised discretisation and Laplace noise applied to feature vectors and responses.
result Strongly universally consistent estimator for regression and classification.
New algorithms improve online prediction from experts with privacy constraints.
problem Online prediction from experts under privacy constraints.
method Proposed and analyzed new algorithms for approximate and pure differential privacy.
result Achieved improved regret bounds for various adversaries.
Estimates conversion probabilities from click sequences with privacy constraints.
problem Training models in advertising with limited direct click-conversion links.
method Formalizes learning from attribution sets, constructs unbiased estimator, applies Empirical Risk Minimization.
result Empirical Risk Minimization achieves generalization guarantees and robustness against prior errors.
Paper tackles federated learning with privacy, enhancing target data analysis.
problem Heterogeneity and privacy of distributed data in federated learning.
method Formulates federated differential privacy, studies statistical problems under privacy constraints.
result Federated differential privacy offers a balance between privacy and knowledge transfer.
Optimal privacy-preserving ranking from noisy comparisons.
problem Protecting individual privacy in ranking from noisy comparisons.
method Differentially private ranking algorithms under edge and individual differential privacy.
result Achieved minimax optimal rates of convergence under privacy constraints.
While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular approach known as **differential privacy** offers provable privacy guarantees, but it is often observed in practice that it could substanti…
Private CI tests for continuous Z with privacy constraints.
problem Testing conditional independence under differential privacy constraints.
method Developed two private CI testing procedures based on generalized covariance and conditional randomization tests.
result First private CI tests with rigorous theoretical guarantees for continuous Z.
We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution p p p and a set of k k k probability distributions Q \mathcal{Q} Q , we aim to output, under the constraints of ε \varepsilon ε -local differential privacy, a distribution from Q \mathcal{Q} Q whos…
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires t…