Privacy-Enhancing Technologies via Applied Cryptography Engineering (PETAce) is a framework for privacy-preserving computing. It provides strong privacy guarantee by analytzing and computing cryptographically pseudonymized data without revealing hidden sensitive information. It consists of the following parts:
The “user interface” layer provides users with high-level programming interfaces for collaborative data analysis (SecureNumpy), joint SQL query (SecureSQL), and privacy-preserving machine learning (SecureML). It also supports large-scale data computing by combining Secure Multi-party Computation and parallel processing systems (such as Spark).
The “virtual machine” layer is responsible for parsing high-level language into secure multi-party computation (MPC) operators, and performing automatic optimization and scheduling.
The “protocol” layer includes secure multi-party computation protocols, such as general-purpose two-party secure computation protocols, privacy set intersection, and privacy information retrieval, etc.
The “primitive” layer consists of standard cryptographic algorithms and protocols, differential privacy mechanisms, and abstract network interfaces, etc.
PETAce enables fast prototyping of ideas based on privacy-enhancing technologies, and we plan to integrate state-of-the-art research results into the PETAce in future releases. Its core modules are implemented in C++ and are modularized into the following repositories.