Researchers found PP-GANs can hide sensitive data in sanitized images, undermining privacy checks.
problem Lack of formal proofs of privacy in PP-GANs for image sanitization.
method Subverted PP-GANs for facial expression recognition to hide sensitive data in sanitized images.
result It is possible to hide sensitive identification data in sanitized PP-GAN output images, even allowing reconstruction of entire input images.
Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial …
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…
DP-CGAN generates private synthetic data and labels.
problem Preserving privacy in synthetic data generation.
method Differentially private conditional GAN (DP-CGAN) with clipping and perturbation.
result DP-CGAN generates visually and empirically promising results on MNIST with low privacy cost.
privGAN protects synthetic data from membership inference attacks.
problem Membership inference attacks on synthetic data generated by GANs.
method Developed a new GAN architecture (privGAN) that not only generates synthetic data but also defends against membership inference attacks.
result privGAN provides protection against membership inference attacks without significantly compromising downstream performance.
Origami uses SGX enclaves and blinding to protect deep neural network inference privacy.
problem Protecting deep neural network inference privacy in machine learning services.
method Combines enclave execution, cryptographic blinding, and accelerator-based computation.
result Demonstrates improved privacy-preserving inference performance compared to prior work.
GANs help create realistic synthetic health data, boosting medical research.
problem Challenges in creating realistic synthetic health data due to private patient data.
method Generative Adversarial Networks (GANs) to learn and produce synthetic health data.
result GANs can produce realistic synthetic health data, overcoming challenges in OHD.
Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by th…
GS-WGAN sanitizes sensitive data for machine learning with improved privacy and model quality.
problem Lack of privacy in sensitive data hinders machine learning applications.
method Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN).
result GS-WGAN generates more informative samples and outperforms state-of-the-art approaches.
A new federated learning method protects privacy in mobile crowdsensing.
problem Data and model privacy protection in federated extreme gradient boosting for mobile crowdsensing.
method Secret sharing based federated learning architecture FedXGB.
result FedXGB achieves less than 1% accuracy loss while preserving model privacy.
FedSyn generates synthetic data from multiple organizations' datasets.
problem Generating diverse synthetic data from limited datasets.
method Federated learning and GAN for privacy-preserving synthetic data generation.
result Synthetic data can be generated from diverse datasets without accessing individual data.
Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robust learning. In this paper, we demonstrate the failure of alternating gradient descent in minimax optimization problems due to the discontinu…
Deep Learning has recently become hugely popular in machine learning, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with …
Develops a privacy-preserving algorithm for sparse robust regression.
problem Privacy-preserving machine learning for sparse robust regression.
method Develops FRAPPE algorithm for non-smooth loss under differential privacy.
result Achieves better privacy and statistical accuracy trade-off.
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.
Privacy-preserving deep learning for medical data across distributed platforms.
problem Data leakage in medical platforms.
method Separates hidden layers; first layer local, others centralized for training.
result Improved learning performance with all data used during training.
An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially …
Paper presents a privacy-preserving method for dynamic assortment selection.
problem Personalized assortment recommendations with data privacy concerns.
method Perturbed upper confidence bound method integrating calibrated noise.
result Policy satisfies Joint Differential Privacy (JDP) with near-optimal regret bound.
This paper analyzes privacy-preserving methods for collaborative forecasting.
problem Data owners' reluctance to share data due to competitive and privacy concerns.
method Examines three groups of privacy-preserving methods: data transformation, secure multi-party computations, and decomposition methods.
result State-of-the-art techniques have limitations in preserving data privacy, such as trade-offs between privacy and forecasting accuracy.
Proposes a privacy-preserving recommendation system using matrix factorization and differential privacy.
problem Privacy leakage in recommendation systems when anonymizing user data is not sufficient.
method Uses matrix factorization and differential privacy via the Gaussian mechanism.
result Demonstrates excellent utility for privacy-preserving recommendation systems.
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs …
Proposes differentially private normalizing flows for privacy-preserving density estimation.
problem Privacy concerns in density estimation models when individuals are directly associated with the training data.
method Uses normalizing flow models with explicit differential privacy guarantees.
result Substantially outperforms previous state-of-the-art approaches in privacy-preserving density estimation.
Proposes using probabilistic models for privacy-preserving synthetic data.
problem Designing high-quality synthetic data for privacy preservation.
method Formulate the problem through probabilistic modelling, choosing a model for the data.
result Statistical discoveries can be reliably reproduced from synthetic data.
Gradient sparsification enhances privacy-preserving machine learning models.
problem Improving performance of differentially-private machine learning models under privacy constraints.
method Gradient sparsification combined with compressed sensing and additive Laplace noise.
result Gradient sparsification can improve performance of differentially-private machine learning models for small privacy budgets.
Tempered sigmoids improve deep learning privacy.
problem Privacy-preserving deep learning with strict differential privacy guarantees.
method Developed tempered sigmoid activation functions for deep learning models.
result Tempered sigmoids outperform ReLU in achieving state-of-the-art accuracy.
Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
problem Addressing mode collapse and likelihood issues in GANs and autoencoders.
method Explains various GAN and autoencoder variants, their applications, and methods to resolve issues.
result Various methods to resolve mode collapse and improve likelihood in GANs and autoencoders.
Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here,…
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often involve sensitive information, thus far there has been no work on privacy-preserving IPW methods. We add…
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches develope…
Three privacy-preserving methods for median regression are proposed.
problem Protecting individual privacy in median regression analysis.
method Three privacy-preserving methods: finite smoothing, iterative, and greedy coordinate descent.
result Numerical results show varying performance across different sample sizes.
This paper applies secure multi-party computation to K-means clustering to protect private data.
problem Privacy-preserving K-means clustering for distributed private data.
method Secure multi-party computation (MPC) techniques to protect private data during K-means clustering.
result Privacy-preserving K-means clustering is feasible and effective for both horizontal and vertical data distribution.
Paper tightens privacy and generalization bounds for iterative learning.
problem Balancing privacy and generalization in iterative learning algorithms.
method Established alignment between generalization and privacy, derived composition theorems for iterative algorithms.
result Generalization bounds for iterative learning algorithms are strictly tighter than existing works.
Paper proposes a privacy-preserving method for estimating complex models.
problem Lack of flexibility in existing model classes for approximating data-generating processes.
method Privacy-preserving distributed estimation of generalized additive mixed models using component-wise gradient boosting.
result Proposed algorithm yields equivalent model estimates as component-wise gradient boosting on pooled data.
A new GAN model α-GAN with tunable loss function addresses gradient vanishing and mode collapse issues.
problem Addressing vanishing gradients and mode collapse in GANs.
method Introduced a tunable GAN α-GAN using a supervised α-loss function. result Holistic understanding of α-GAN related to Arimoto divergence and convergence properties. Unbalanced GANs stabilize GAN training by pre-training the generator with VAE.
problem Stable training of GANs to avoid mode collapses and improve image quality.
method Pre-train GAN generator with VAE, balance generator and discriminator training, prevent discriminator's early convergence.
result Unbalanced GANs reduce mode collapses and outperform ordinary GANs in stability, convergence, and image quality.
GANs can approximate SDEs for large time steps.
problem Approximating SDEs for large time steps using GANs.
method Proposed a conditional GAN architecture to enable strong approximation of SDEs.
result Supervised GAN outperformed standard GAN and other schemes in strong error.
Efficiently preserves privacy in logistic regression for IoT data.
problem Balancing data privacy and utility in collaborative learning.
method Matrix encryption approach for secure multi-party computation.
result Proposes a privacy-preserving logistic regression model with fast convergence.
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information. Our approach employs adversarially-trained neural networks to implement random…
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.
Paper presents privacy-preserving techniques for HD computing.
problem Privacy loss in HD computing due to reversible computation.
method Quantization and pruning of hypervectors for differential privacy.
result Differentially private HD model for cloud inference.
While many solutions for privacy-preserving convex empirical risk minimization (ERM) have been developed, privacy-preserving nonconvex ERM remains a challenge. We study nonconvex ERM, which takes the form of minimizing a finite-sum of nonconvex loss functions over a training set. We propose a new differentially private…
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze the \emph{consistency} and \emph{privacy-preservation}. Instead of deterministic greedy split rule…
VFGNN tackles privacy-preserving node classification with federated GNN.
problem Data isolation problem in graph data.
method Vertically partitioned federated GNN, differential privacy.
result Demonstrates effectiveness of VFGNN on three benchmarks.
Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate training. To adequately protect sensitive information, we propose distributed layer-partitioned train…
Improves privacy guarantees by analyzing randomness in privacy-preserving mechanisms.
problem Balancing user privacy and business constraints in privacy-preserving mechanisms.
method Analyzes explicit and implicit randomness in privacy mechanisms and proposes a probabilistic calibration method.
result Proposes privacy at risk, providing stronger privacy guarantees with quantifiable risks.
Framework prevents data leakage in mobile cloud DNNs.
problem Data leakage from cloud DNNs poses privacy risks.
method Privacy-preserving reinforcement learning framework.
result Framework successfully defends against various privacy attacks.
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an f-divergence (f-GANs) or an integral probability metric (Wasserstein GANs) between the model and the data distribution using a discriminator. Wasserstein GANs en…
Secure Multiparty Computation protects data privacy in Symbolic Regression.
problem Data privacy in Symbolic Regression models.
method Secure Multiparty Computation for vertical partitioning.
result Comparable performance to centralized model while preserving privacy.