Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modelling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also including prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key data sets for research.
Code (2)
Tasks
Privacy PreservingSynthetic Data GenerationSimilar Papers 제목 키워드 기반
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for mode…
Federated LearningPrivacy PreservingPrivacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning
Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required fo…
Decision MakingMulti-agent Reinforcement LearningPrivacy PreservingAnalysing Fairness of Privacy-Utility Mobility Models
Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories. Existing privacy techniques tend to propose ideal privacy-utility tr…
FairnessPrivacy PreservingRepresentation LearningPrivacy-Preserving Distributed Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks …
Privacy PreservingDifferentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
The increasing demand for privacy-preserving data analytics in finance necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce DP-Fed-FinDiff framework, a novel integra…
DenoisingFederated LearningPrivacy PreservingSynthetic Data Generation+1