Research Area:  Blockchain Technology

Abstract:

Structural data have played a prominent role in various domain technologies, such as social networks, criminal tracking, epidemic spreading, and cheminformatics. Secure multi-party computation (MPC) is an indispensable research topic in the blockchain. Privacy within its concerns restricts the access to network data with sensitive information. Therefore, it is imperative to study the private computing of graph algorithms, in which the subgraph of private graphs is of great significance. The subgraph isomorphism has not yet been proved as an NP-complete problem. This paper gives an insight into solving the MPC protocol of subgraph matching. Besides, it designs a new encoding method to represent a graph and studies the MPC to determine whether one graph is sub-isomorphic to the other by both parties to collaborative compute through a homomorphic encryption method and a decryption technique. Also, this protocol is proposed for security preserving in the semi-honest model, and the computational complexity of the proposed protocol is shown to be decreased. Both the theoretical analysis and experimental results represent that the proposed protocol is effective and efficient. The protocol has been implemented in a real-time blockchain project.

Keywords:  

Author(s) Name:  Mengjiao Guo , Chi-Hung Chi , Hui Zheng , Kai Zhang , Jing He

Publisher name:  ACM

DOI:  10.1145/3498851.3498994

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3498851.3498994