Low-Complexity Symmetric Cryptographic Primitives

 Privacy are today at the heart of many societal discussions. In the era of big data and the Internet of Things, our personal data have been collected, analyzed and exploited by intelligence agencies and IT companies. Encrypting personal data is a good practice for protecting privacy. Due to efficiency in implementation, symmetric cryptography has been widely used to protect the confidentiality of private data. However, protecting privacy with classical symmetric encryption algorithms usually destroys the usability of the data and disables the functionalities of cloud computing and data analysis based on machine learning. 
To reconcile privacy and usability, advanced cryptographic protocols have been proposed to allow computing on encrypted data. Emerging privacy-preserving protocols include Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), ZeroKnowledge proofs (ZK). However, the standard symmetric primitives are not efficient in the context of MPC, FHE and ZK. As alternatives, symmetric cryptographic primitives with low algebraic or arithmetic complexity have been developed to offer more efficient implementations of MPC, FHE and ZK.
This project aims to study the MPC/FHE/ZK-friendly low-complexity symmetric key primitives. The main purpose of this project is to develop new methods in the analysis and design of low-complexity symmetric primitives. The results will shed light on how to improve privacy-preserving protocols