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Found 9 papers, 1 papers with code
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series
During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner.
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure.
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices.
Evaluation of Inference Attack Models for Deep Learning on Medical Data
This paper aims to attract interest from researchers in the medical deep learning community to this important problem.
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
To address this issue, we propose in this paper the first DP version of (Gradient) EM algorithm with statistical guarantees.
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data.
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee.
Differentially Private and Fair Classification via Calibrated Functional Mechanism
In order to enforce $\epsilon$-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint.
Differentially Private ADMM for Distributed Medical Machine Learning
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests.
