Dynamic Graph Publication With Differential Privacy Guarantees for Decentralized Applications

Zhetao Li, Yong Xiao, Haolin Liu*, Xiaofei Liao, Ye Yuan, Junzhao Du

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Decentralized Applications (DApps) have garnered significant attention due to their decentralization, anonymity, and data autonomy. However, these systems face potential privacy challenge. The privacy challenge arises from the necessity for external service providers to collect and process user interaction data. The untrustworthiness of these providers may lead to privacy breaches, compromising the overall security of such DApp environments. To address this challenge, we model the interaction data in the DApp environments as dynamic graphs and propose a dynamic graph publication method named HMG (Hidden Markov Model for Dynamic Graphs). HMG estimates the interaction probabilities between users by extracting the temporal information from historically collected data and constructs an optimized model to generate synthetic graphs. The synthetic graphs can preserve the dynamic topological characteristics of the interaction processes within DApp environments while effectively protecting user privacy, thus assisting external service providers in performing effective analyses. Finally, we evaluate the performance of HMG using real-world datasets and benchmark it against commonly used graph metrics. The results demonstrate that the synthetic graphs preserve essential features, making them suitable for analysis by service providers.

Original languageEnglish
Pages (from-to)1771-1785
Number of pages15
JournalIEEE Transactions on Computers
Volume74
Issue number5
DOIs
Publication statusPublished - 2025
Externally publishedYes

Keywords

  • DApp
  • Graph publication
  • hidden Markov model
  • local differential privacy

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