Zero-knowledge proof: Creating trust between users on digital platforms

A new cryptographic tool developed at Texas A&M University could help to raise the levels of trust between users of cloud computing, blockchains, machine-learning services and other digital platforms.

Yupeng Zhang, assistant professor in the Department of Computer Science and Engineering, recently received the National Science Foundation’s Faculty Early Career Development (CAREER) Award for his research project, “Towards Efficient and Scalable Zero-Knowledge Proofs.”

The focus of his research is on developing efficient and scalable zero-knowledge proof schemes, an important cryptographic primitive (well-established, low-level cryptographic algorithms used to build cryptographic protocols) to ensure data privacy and computation integrity simultaneously.

In cryptography, a zero-knowledge proof is a method by which one party (the prover) can prove to another party (the verifier) that a given statement is true while the prover avoids conveying any additional information apart from the fact that the statement is indeed true. The essence of zero-knowledge proof is that it is trivial to prove that one possesses knowledge of certain information by simply revealing it; the challenge is to prove such possession without revealing the information itself or any additional information.

“My research is to enhance the security, privacy and integrity of data and computations in the digital world through schemes in the area of cryptography,” he said. “In this award, ‘zero-knowledge proof’ is a powerful tool to establish trust between people without knowing each other ahead of time. It allows one to convince others that their secret data has some properties, without revealing the secret data itself. Because of this powerful functionality, zero-knowledge proofs have found great applications in cutting-edge technologies to provide privacy, scalability and integrity.”

Zhang’s project advances three aspects of the zero-knowledge proof schemes: theory, application and systems. On the theory side, new practical schemes with linear running time in the size of the computation are constructed based on error-correcting codes and expander graphs. On the application side, the project investigates machine-learning algorithms and graph algorithms and develops efficient zero-knowledge proofs tailored for these applications. On the system side, the project initiates the study of memory-efficient and distributed algorithms for zero-knowledge proofs.