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Found 10 papers, 4 papers with code
Towards Biologically Plausible and Private Gene Expression Data Generation
In this paper, we initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data.
Privacy-Aware Document Visual Question Answering
We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected.
A Unified View of Differentially Private Deep Generative Modeling
The availability of rich and vast data sources has greatly advanced machine learning applications in various domains.
Client-specific Property Inference against Secure Aggregation in Federated Learning
We formulate an optimization problem across different rounds in order to infer a tested property of every client from the output of the linear models, for example, whether they have a specific sample in their training data (membership inference) or whether they misbehave and attempt to degrade the performance of the common model by poisoning attacks.
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
This work proposes FedLAP-DP, a novel privacy-preserving approach for federated learning.
Private Set Generation with Discriminative Information
Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy guarantees, which is essential for scientific progress in sensitive domains.
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data
Survival analysis or time-to-event analysis aims to model and predict the time it takes for an event of interest to happen in a population or an individual.
Constrained Differentially Private Federated Learning for Low-bandwidth Devices
This bandwidth and corresponding processing costs could be prohibitive if the participating entities are, for example, mobile devices.
Compression Boosts Differentially Private Federated Learning
In this paper, compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy.
Federated Learning in Adversarial Settings
This paper presents a new federated learning scheme that provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy.
