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 algorithms for community detection in graphs with privacy constraints.
problem Community recovery in stochastic block models with node-wise privacy.
method Spectral clustering with privacy mechanisms, including privatized PCA, convex optimization, and matrix estimation.
result Developed algorithms that are computable in polynomial-time and achieve consistent community estimation under node differential privacy.
Develops a private synthetic graph generator using Gromov-Wasserstein distance.
problem Creating private synthetic networks for complex data.
method Random connection model, fused Gromov-Wasserstein distance, differential privacy.
result Effective algorithm for generating private synthetic graphs with theoretical guarantees.
New algorithms learn graph structures privately, matching best results.
problem Private learning of graph structures with multiple blocks.
method Sum-of-squares relaxation and exponential mechanism for score function.
result Matches statistical utility of previous best non-private methods.
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.
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
Privacy-preserving GNNs for graph data with sensitive node data.
problem Privacy concerns in learning node representations for graphs with sensitive data.
method Developed a privacy-preserving GNN learning algorithm based on Local Differential Privacy (LDP). Proposed an LDP encoder, an unbiased rectifier, and a denoising mechanism (KProp).
result Our method maintains a satisfying level of accuracy with low privacy loss.
APGE protects graph node representations from inference attacks.
problem Privacy leakage in graph embedding methods.
method Adversarial training framework with disentangling and purging mechanisms.
result APGE preserves structural and utility attributes while concealing private information.
Directed graphical models (DGMs) are a class of probabilistic models that are widely used for predictive analysis in sensitive domains, such as medical diagnostics. In this paper we present an algorithm for differentially private learning of the parameters of a DGM with a publicly known graph structure over fully obser…
We consider a network of agents that aim to learn some unknown state of the world using private observations and exchange of beliefs. At each time, agents observe private signals generated based on the true unknown state. Each agent might not be able to distinguish the true state based only on her private observations.…
For graphs generated from stochastic blockmodels, adjacency spectral embedding is asymptotically consistent. Further, adjacency spectral embedding composed with universally consistent classifiers is universally consistent to achieve the Bayes error. However when the graph contains private or sensitive information, trea…
Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our …
Polynomial-time algorithm estimates edge density of random graphs with privacy and robustness.
problem Estimating edge density of random graphs while maintaining privacy and robustness.
method Sum-of-squares algorithm for robust edge density estimation and reduction from privacy to robustness.
result Optimal error rate up to logarithmic factors, matching theoretical lower bounds.
The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as raises serious concerns for the privacy of individuals. In this paper, we propose a massively distributed protocol for a large set of users …
Privacy constraints affect learning Markov Random Fields differently.
problem Learning Markov Random Fields under differential privacy constraints.
method Algorithms for structure and parameter learning under pure, concentrated, and approximate differential privacy.
result Privacy constraints impose a strong separation between structure and parameter learning in high-dimensional data.
We consider a group of Bayesian agents who try to estimate a state of the world θ through interaction on a social network. Each agent v initially receives a private measurement of θ: a number Sv picked from a Gaussian distribution with mean θ and standard deviation one. Then, in each discrete time iteration,…
We investigate the problem of nodes clustering under privacy constraints when representing a dataset as a graph. Our contribution is threefold. First we formally define the concept of differential privacy for structured databases such as graphs, and give an alternative definition based on a new neighborhood notion betw…
Enhances privacy in federated learning with Laplacian smoothing.
problem Protecting data privacy in federated learning while maintaining model accuracy.
method Laplacian smoothing for differentially private federated learning (DP-Fed-LS).
result Improves model accuracy with differential privacy guarantee and membership privacy.
New method recovers graph latent positions under edge differential privacy.
problem Recovering latent graph information from privatized graphs.
method Applying geometric insights to adjust statistical inference for privatized graphs.
result Achieves consistent recovery of latent positions under local edge differential privacy constraints.
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.
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.
Improved algorithm for selecting a hypothesis locally privately with fewer queries.
problem Locally private hypothesis selection with minimal privacy-preserving queries.
method Introduces a Scheffé graph to reduce query complexity for hypothesis selection.
result Algorithm performs O~(k3/2) queries, improving on previous methods. 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.
A novel approach to federated learning with strong privacy guarantees.
problem Maintaining privacy of clients' data and federator's objective in federated learning.
method Inspired by knowledge distillation and private information retrieval, the approach combines secret-sharing-based multi-party computation and graph-based private information retrieval.
result Strong information-theoretic privacy guarantees for federated learning.
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.
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…
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.
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.
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.
We study the relationship between the notions of differentially private learning and online learning in games. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}…
New findings show limitations in converting private learning to online learning efficiently.
problem Limitations in converting private learning to online learning efficiently.
method Assuming one-way functions, we show an efficient conversion from pure-private learners to online learners is impossible.
result Efficient conversion from pure-private learners to online learners is impossible under certain assumptions.
Fixed-parameter tractability of private synthetic data generation
problem Generating synthetic data under differential privacy
method Linear programming and subsampled private multiplicative weights method
result Optimal error rates across all regimes
SBO uses dual voting to build consensus in noisy feedback settings.
problem Collective decision-making under social influence.
method Dual voting system with noisy public votes and private interviews.
result Efficient consensus-building through noisy public votes, debiased by estimated social graph.
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.
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.
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.
Private learning can perform well in high dimensions, contrary to known results.
problem When does differentially private learning not suffer in high dimensions?
method Introduced a condition called restricted Lipschitz continuity to derive improved bounds for excess empirical and population risks.
result Gradients in private fine-tuning of large models are mostly controlled by a few principal components, similar to conditions for convex settings.
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.
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.
Study privacy and robustness in learning halfspaces, proving hard trade-offs.
problem Balancing privacy and robustness in learning halfspaces.
method Proves nearly tight bounds on sample complexity for robust private learning of halfspaces.
result Robust and private learning is harder than robust or private learning alone.
Paper develops a private algorithm for multi-agent learning in bandits.
problem Private cooperative learning in decentralized systems.
method Developed extsc{FedUCB} algorithm for multi-agent learning.
result Improves pseudoregret bounds and empirical performance.
New private algorithms for online learning improve regret in high privacy regimes.
problem Private online learning from experts and convex optimization.
method Transformed lazy algorithms for differential privacy.
result Improved regret bounds for DP-OPE and DP-OCO.
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.
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.
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.
Private machine learning framework using randomised response.
problem Private machine learning in adversarial environments.
method Randomised response for noisy data release.
result Consistent estimation of true machine learning model.
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.
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.