Privacy preserving federated big data analysis

Biomedical data are often collected and hosted by different agents (e.g., hospitals, insurance company, sequencing centers). For example, regional hospitals have the data from local patients. On the other hand, data from the same patient can also spread across multiple hospitals and institutions when she makes multiple visits. There are many benefits to use distributed data together in research studies but it is challenging to pool the raw data due to efficiency and privacy concerns. We review solutions for privacy-preserving federated data analysis. To better explain these solutions, we begin with the architectures and optimization methods. We present the server/client and decentralized architectures to perform privacy-preserving federated data analysis. Under these architectures, the Newton-Raphson method and alternating direction method of multipliers (ADMM) framework are introduced …