Privacy-Preserving Technologies in Data

Privacy-Preserving Technologies in Data is a research topic within Artificial Intelligence. Science Explorer counts 35k research works in it since 1953. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy. The papers explore methods to protect sensitive information while performing data mining, machine learning, and statistical analysis.

• Differential Privacy
• Federated Learning
• k-Anonymity
• Privacy Preservation
• Machine Learning
• Location Privacy
• Data Mining
• Anonymization
• Secure Computation
• Membership Inference Attacks