Privacy Preserving
499 papers with code • 0 benchmarks • 1 datasets
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Libraries
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ConcurrentQA Benchmark
Most implemented papers
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Federated Causal Discovery From Interventions
We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
FedGH: Heterogeneous Federated Learning with Generalized Global Header
lipingyi/fedgh
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It is a communication and computation-efficient model-heterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models at the FL server.
A Platform for the Biomedical Application of Large Language Models
Current-generation Large Language Models (LLMs) have stirred enormous interest in recent months, yielding great potential for accessibility and automation, while simultaneously posing significant challenges and risk of misuse.
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
Differentially Private Releasing via Deep Generative Model (Technical Report)
alps-lab/dpgan
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Privacy-preserving releasing of complex data (e. g., image, text, audio) represents a long-standing challenge for the data mining research community.
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol.
Distributed Differentially Private Computation of Functions with Correlated Noise
CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems.
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
ai-secure/g-pate
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NeurIPS 2021
In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator.
ER-AE: Differentially Private Text Generation for Authorship Anonymization
McGill-DMaS/AuthorshipAnonymization
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NAACL 2021
By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style.
