New framework enhances privacy in federated learning for sensitive data

Researchers have developed a new framework called the Gaussian Privacy Protector (GPP) designed to enhance privacy in data release, particularly for continuous, high-dimensional inputs. GPP utilizes a stochastic encoder to map raw data to a lower-dimensional, sanitized representation. This process is trained to minimize the mutual information between the sanitized data and sensitive attributes while preserving utility attributes, with a tunable parameter controlling this trade-off. The framework has also been extended to a federated learning setting, offering instance-level privacy protection beyond the standard guarantees of federated learning. AI

IMPACT Introduces a novel method for protecting sensitive attributes in released datasets, potentially enabling broader use of sensitive data for AI model training.

RANK_REASON This is a research paper detailing a new privacy-preserving data release framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Gaussian Privacy Protector
• HAPT-Recognition
• MNIST
• Zahir Alsulaimawi
• CelebA

• paper
• safety

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