Ant Group Open Sources Privacy-Preserving Computation Framework


SecretFlow can also be deployed in cluster mode, which enables allocating nodes to specific users to increase privacy

Alibaba financial arm Ant Group has open-sourced SecretFlow, its privacy-preserving framework, with a specific focus on data analysis and machine learning.

SecretFlow includes several components, such as a secure processing unit, which provides specific computation capabilities guaranteeing data privacy; a homomorphic encryption unit; a portable most straightforward oblivious transfer protocol implementation; and SecretFlow, a higher-level unified framework integrating all of them. While the high-level SecretFlow module is written in Python, the lower-level modules are written in C, C++, and assembly.

SecretFlow aims to be complete, transparent, open, and interoperable with other technologies. According to the Ant Group, the framework aims to make it easier for developers to create applications based on privacy-preserving computing and contribute to the market’s further growth and technology maturity.

SecretFlow can also be deployed in cluster mode, allocating nodes to specific users to increase privacy. SecretFlow cluster mode is based on Ray, an open source framework that provides a simple, universal API for building distributed applications.

For a quick start with SecretFlow, you can check the tutorials, which present a number of use cases, from data preprocessing to logistic regression, to neural network training, and so on.

Privacy-preserving computation is a technique that aims to protect sensitive data while they are processed. Using such techniques, e.g., homomorphic encryption, you can carry through computation over encrypted data, which ensures it cannot be collected or tampered with during the processing.