Differential privacy improves AI security, fairness, and learning.
problem Privacy violations, security issues, and model fairness in AI.
method Application of differential privacy in various AI areas.
result Differential privacy enhances AI performance in multiple areas.
Addresses theoretical and practical aspects of Gaussian differential privacy.
problem Theoretical and practical challenges in privacy-preserving data analysis.
method Discussion of f-differential privacy and Gaussian differential privacy.
result Gaussian differential privacy can enhance privacy in various applications.
Survey on privacy issues in deep learning and proposed solutions.
problem Privacy concerns in deep learning models due to sensitive data.
method Review of existing privacy techniques and gaps in research.
result Identification of test-time inference privacy as a research gap.
This paper addresses privacy issues in personalized pricing using nonparametric demand models.
problem Privacy violation in personalized pricing algorithms with unknown nonparametric demand models.
method Develops algorithms to make pricing decisions and learn demand while ensuring central and local differential privacy.
result Proves near-optimal regret bounds for algorithms with CDP and LDP guarantees.
DPNR preserves privacy of text representations using differential privacy.
problem Privacy leakage in deep learning text representations.
method DPNR uses Differential Privacy to provide formal privacy guarantees and dropout masking for enhanced privacy.
result DPNR reduces privacy leakage without significantly sacrificing main task performance.
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.
Introduces Relational Privacy (RP) to control relation memorization in question answering models.
problem Relation memorization in question answering models can lead to privacy issues.
method Formalizes Relational Privacy (RP) and Differential Relational Privacy (DrP), providing bounds on relation memorization.
result DrP allows effective learning of general properties of underlying concepts while preventing relation memorization.
Significant success has been realized recently on applying machine learning to real-world applications. There have also been corresponding concerns on the privacy of training data, which relates to data security and confidentiality issues. Differential privacy provides a principled and rigorous privacy guarantee on mac…
Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address p…
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive inf…
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
Guaranteeing a certain level of user privacy in an arbitrary piece of text is a challenging issue. However, with this challenge comes the potential of unlocking access to vast data stores for training machine learning models and supporting data driven decisions. We address this problem through the lens of dx-privacy, a…
Enhances privacy in machine learning through Rényi Pufferfish mechanisms.
problem Designing general and efficient Pufferfish mechanisms that maintain privacy and utility.
method Introduces a Rényi divergence-based variant of Pufferfish, generalizes the Wasserstein mechanism, and proves privacy amplification results.
result Extends the applicability of Pufferfish framework and provides stronger privacy guarantees.
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
New approach connects robust learning to privacy issues.
problem Vulnerability of deep neural networks to adversarial examples.
method Privacy-utility tradeoff and rate-distortion theory.
result Found saddle point via maximum conditional entropy problem.
New analysis shows SGD with noise doesn't leak more privacy with more iterations.
problem Privacy loss in noisy SGD with more iterations.
method Privacy Amplification by Iteration and Sampled Gaussian Mechanism.
result Privacy loss remains constant after a burn-in period, not increasing with more iterations.
Paper tackles privacy-preserving data density issues using deconvolution.
problem Privacy-preserving noise affects data density, leading to under/over-estimation.
method Develops deconvoluting kernel density estimators and regression models.
result Demonstrates improved accuracy in estimating heavy-hitters with locally differential data.
FEDMD-NFDP improves federated learning privacy without sacrificing performance.
problem Privacy leakage in federated learning when sharing predictions.
method Noise-Free Differential Privacy (NFDP) applied to federated model distillation.
result FEDMD-NFDP achieves comparable utility and privacy guarantees.
Privacy is crucial in many applications of machine learning. Legal, ethical and societal issues restrict the sharing of sensitive data making it difficult to learn from datasets that are partitioned between many parties. One important instance of such a distributed setting arises when information about each record in t…
This paper studies fairness and privacy in federated learning, proposing algorithms to balance both.
problem Joint impact of differential privacy and fairness in federated classification.
method Proposes FDP-Fair and CDP-Fair algorithms for demographic disparity constrained classification under federated differential privacy.
result Established theoretical guarantees on privacy, fairness, and excess risk control.
The paper explores how to measure and optimize ad reach while maintaining user privacy.
problem Measuring ad reach while preserving user privacy in online advertising.
method Introduces k-anonymity and probabilistic discounting for frequency capping. result Privacy introduces a significant performance drop but with manageable costs.
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models. These tools provide a promising direction in the studies where data availability is limited. One common issue i…
Score attack method provides a lower bound on privacy-constrained minimax risk.
problem Characterizing the optimality of privacy-constrained statistical models.
method Score attack based on tracing attack concept.
result Optimally lower bounds the minimax risk of estimating unknown model parameters.
Synth-MIA assesses privacy leakage in synthetic tabular data models.
problem Challenges in evaluating privacy leakage in synthetic tabular data.
method Unified threat framework deploying multiple attacks.
result Higher synthetic data quality correlates with greater privacy leakage.
New method preserves privacy while improving machine learning accuracy.
problem Privacy-preserving machine learning for daily data.
method Compressive Privacy and multi-kernel method.
result Improved utility classification accuracy with privacy preservation.
With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML…
DPlis improves privacy in deep learning models by smoothing loss functions.
problem Privacy leakage in deep learning models trained on private data and low model performance.
method DPlis constructs a smooth loss function to favor noise-resilient models.
result DPlis effectively boosts model quality and training stability under privacy constraints.
This paper enhances privacy-preserving randomized power method for large datasets.
problem Privacy issues in applying randomized power method to large datasets containing personal information.
method Proposes enhanced privacy-preserving variants of the randomized power method, including a variant with reduced noise and a decentralized framework.
result Tighter convergence bounds and empirical comparisons with previous work in real recommendation datasets.
P3GM improves privacy-preserving data synthesis for high-dimensional data.
problem Mitigating privacy risks in releasing large volumes of sensitive data.
method Privacy-preserving phased generative model (P3GM) with two-phase learning process.
result P3GM significantly outperforms existing solutions in terms of noise reduction and data accuracy.
SAPAG attacks distributed learning by reconstructing true training data from gradients.
problem Privacy attacks on distributed learning systems through gradients.
method SAPAG uses a Gaussian kernel-based gradient difference distance measure.
result SAPAG can reconstruct training data on various DNNs and at different training phases.
This paper optimizes SMPC for neural network inference, reducing memory and time.
problem Memory and time constraints in secure neural network inference.
method Implemented ABY2.0 protocol, optimized memory usage, and used a helper node.
result MNIST inference reduced from 8.03 GB RAM and 200s to 0.2 GB RAM and 32s.
In machine learning, boosting is one of the most popular methods that designed to combine multiple base learners to a superior one. The well-known Boosted Decision Tree classifier, has been widely adopted in many areas. In the big data era, the data held by individual and entities, like personal images, browsing histor…
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.
Paper develops a new method for differential privacy sampling using Wasserstein distance.
problem Sampling from distributions under differential privacy constraints with geometric structure consideration.
method Develops a novel framework with Wasserstein Projection Mechanism (WPM) for minimax optimal mechanisms.
result Proposes efficient algorithms for approximate computation of the Wasserstein Projection Mechanism.
We consider membership inference attacks, one of the main privacy issues in machine learning. These recently developed attacks have been proven successful in determining, with confidence better than a random guess, whether a given sample belongs to the dataset on which the attacked machine learning model was trained. S…
This paper improves privacy and fairness in federated learning by protecting sensitive data and ensuring group fairness.
problem Privacy and fairness issues in federated learning.
method Introduces group privacy through d-privacy, a localized form of differential privacy. result The method provides better group fairness than a global model in federated learning.
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…
Study evaluates federated learning with differential privacy on MIMIC-III, improving model performance with careful parameter tuning.
problem Training machine learning models on privacy-sensitive data sets locked in healthcare facilities.
method Extensive evaluation of federated and differential privacy techniques on MIMIC-III dataset, analyzing various parameters.
result Careful parameter tuning is crucial for federated learning with differential privacy, especially for data distribution and communication strategies.
DMF improves POI recommendation privacy and efficiency.
problem Privacy leaks and high computation/storage costs in centralized MF.
method Decentralized Matrix Factorization (DMF) with random walk training.
result DMF significantly improves recommendation performance.
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federate…
Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distri…
This paper presents a method to generate synthetic data with differential privacy to protect user privacy while maintaining data trends.
problem Protecting user privacy while using real data for AI applications.
method Develops a practical guide to generating synthetic data using differential privacy.
result Synthetic data can be generated to preserve trends of real data while ensuring strong privacy protections.
Paper develops efficient mechanisms for estimating variance and covariance under differential privacy in the add-remove model.
problem Estimating variance and covariance under differential privacy in the add-remove model.
method Developed mechanisms based on the Bézier mechanism, a novel moment-release framework.
result Proved minimax optimality of the Bézier-based estimator in the high-privacy regime and demonstrated its better utility in instance-wise analysis.
Improved method finds second-order stationary points privately with better efficiency.
problem Finding second-order stationary points privately under differential privacy constraints.
method Adaptive batch sizes and binary tree mechanism.
result Improved bound for privately finding SOSP, matching state-of-the-art for FOSP.
The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in …
Graph-based multimodal federated learning for HAR improves accuracy and privacy.
problem Challenges in HAR due to noisy data, incomplete measurements, and privacy concerns.
method Proposes GraMFedDHAR, a Graph-based Multimodal Federated Learning framework for HAR tasks, using modality-specific graphs, residual GCNs, and attention-based fusion.
result Experimental results show up to 13 percent performance improvement for MultiModalGCN under differential privacy constraints.
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided bas…
A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable privacy guarantees of differential privacy which has been combined with Gaussian processes through the previously published \emph{cloaking metho…