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.
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.
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 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.
Paper discusses privacy issues in IoT and proposes a lightweight neural network approach.
problem Privacy concerns in IoT due to extensive data collection and processing.
method Developed a privacy-preserving inference approach for IoT objects and a deep neural network in the cloud.
result Satisfactory performance of the proposed approach on the MNIST dataset.
FedGP uses GANs to generate private data samples in federated learning.
problem Privacy-preserving data release in federated learning.
method FedGP employs generative adversarial networks (GANs) with FedAvg for privacy-preserving data generation.
result FedGP generates high-quality labelled data that can train and validate supervised models securely.
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.
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.
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.
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.
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…
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.
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.
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.
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.
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.
Paper presents a new method for privacy-preserving GLMs on vertically partitioned data.
problem Privacy concerns and data sharing restrictions in collaborative data mining.
method Distributed block coordinate descent algorithm for generalized linear models.
result The method achieves accurate standard errors without additional communication cost.
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.
Missing data enhances privacy in differential privacy.
problem Privacy preservation in datasets with missing values.
method Formalized missing data as a privacy amplification mechanism within differential privacy.
result Incomplete data can yield privacy amplification for differentially private algorithms.
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.
P2B improves local agent performance with differential privacy.
problem Improving local agent performance with differential privacy.
method Differential privacy technique to collect and update local agents' feedback.
result Competitive performance on synthetic and real-world data.
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.
Privacy-preserving eye tracking framework using synthetic images.
problem Preserving sensitive personal information in eye tracking technology.
method Randomized encoding for privacy, Support Vector Regression model training on synthetic eye images.
result Framework achieves real-time gaze estimation with same accuracy as non-private version.
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 …
Federated Forest tackles privacy-preserving machine learning for scattered data.
problem Privacy and data integration challenges for AI applications.
method Proposes Federated Forest, a privacy-preserving machine learning model based on random forest.
result Achieves the same accuracy as non-privacy-preserving models without data exchange.
Privacy-preserving boosting algorithm for machine learning.
problem Privacy concerns in machine learning with sensitive data.
method Local Differential Privacy to protect data privacy while boosting.
result Effective privacy-preserving boosting algorithm developed.
DP-FedTabDiff generates private synthetic tabular data using diffusion models and differential privacy.
problem Privacy-preserving synthetic data generation for tabular data in regulated domains.
method Combines Differential Privacy, Federated Learning, and Denoising Diffusion Probabilistic Models.
result Achieves significant privacy improvements without compromising data quality.
Efficient framework for training machine learning models at edge without data movement.
problem Lack of privacy-preserving and computationally efficient methods for deep learning model training.
method Privacy preserving FedCollabNN framework for federated learning.
result Framework is computationally efficient and robust against adversarial attacks.
Privacy-preserving method protects user speech data from cloud services.
problem Privacy compromise in cloud-based speech analysis.
method Collects and sanitizes speech data before sharing, using transformation functions and voice conversion.
result Identification of sensitive emotional state reduced by ~96%.
Paper proposes scalable privacy-preserving DNN for industrial applications.
problem Data isolation and scalability issues in deep neural networks.
method Split computation graph into private and neutral server parts; use cryptographic techniques for private data.
result Demonstrates practicality of the proposed scalable privacy-preserving DNN.
A framework for privacy-preserving DNN pruning and acceleration.
problem Privacy concerns in DNN weight pruning for mobile devices.
method ADMM-based iterative pruning with synthetic data, compiler optimizations.
result 4.2X, 2.5X, and 2.0X speedup with almost no accuracy loss.
Federated ABC-GMM boosts classification performance in imbalanced clinical data.
problem Class imbalance and privacy concerns in federated learning for clinical data.
method Approximate Bayesian computation-based Gaussian Mixture Model (Federated ABC-GMM) for privacy-preserving oversampling.
result Federated ABC-GMM improves F1 score up to nearly ideal performance in imbalanced clinical data.
A privacy-preserving framework detects faults in circular economy processes.
problem Lack of shared data across company borders due to privacy concerns.
method Federated Principal Component Analysis (PCA) and Secure Multiparty Computation.
result The proposed FedMSPC framework outperforms standard PCA in fault detection.
Study on privacy-preserving health care models that sacrifice accuracy for data protection.
problem Privacy-preserving models in health care neglect data from the tails, reducing accuracy for small groups.
method Used state-of-the-art differentially private learning methods for clinical prediction tasks.
result Privacy-preserving models in health care exhibit steep tradeoffs between privacy and utility, and disproportionately influence large demographic groups.
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,…
Privacy-preserving synthetic data from EHRs for learning and inference.
problem Sharing sensitive EHR data while maintaining patient privacy.
method Differentially private normalizing flows for density estimation and variational inference.
result Privacy-preserving synthetic data can yield good utility at a reasonable privacy cost.
Paper tackles privacy in active learning for sensitive data.
problem Privacy leak in active learning with sensitive data.
method Privacy-preserving active learning approach with quantifiable guarantees.
result Tradeoff between privacy, utility, and annotation budget demonstrated.
Novel framework for privacy-preserving IPW methods in observational studies.
problem Estimating causal effects from observational studies while preserving privacy.
method Proposes a novel framework for privacy-preserving inverse probability weighting (PP-IPW) methods.
result Theoretical and empirical results support the effectiveness of PP-IPW methods.
Paper introduces privacy-preserving inventory policy learning for feature-based newsvendor with unknown demand.
problem Privacy-preserving inventory policy learning for feature-based newsvendor with unknown demand distribution and nonsmooth loss function.
method Developed a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation within f-differential privacy framework.
result Achieved privacy-preserving optimal inventory policy with provable privacy guarantees and desirable statistical precision.
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.
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.
Proposes a privacy-preserving method for graph embedding.
problem Privacy leakage in adjacency spectral embedding for stochastic blockmodels.
method Differentially private adjacency spectral embedding algorithm for stochastic blockmodels.
result Estimates latent positions close to those by non-private embedding, maintaining accuracy at desired privacy levels.
FedSTaS stratifies and samples clients for efficient FL.
problem Inefficient client sampling in federated learning.
method Stratifies clients based on compressed gradients, uses Neyman allocation for sampling, and samples local data uniformly.
result FedSTaS achieves higher accuracy than FedSTS in fixed training rounds.
A collaborative machine teaching method that improves learner performance with privacy and efficiency.
problem Improving learner performance with distributed teachers while maintaining privacy and scalability.
method Formulates collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.
result The proposed method delivers significantly more accurate teaching results with high speed compared to non-collaborative MINLP-based super teaching.
PrivacyFL simulates privacy-preserving federated learning.
problem Ensuring privacy in federated learning environments.
method Extensible, configurable simulator for federated learning.
result PrivacyFL checks feasibility and improves model accuracy.
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.
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.
This review explores federated learning for IoT data privacy.
problem Privacy risks and data transfer costs in IoT data analytics.
method Federated learning approach to protect privacy and reduce data transfer.
result Survey of methods for improving communication efficiency and privacy in IoT federated learning.