Title:

Locally Differentially Private Analysis of Graph Statistics

 

Abstract:

Differentially private analysis of graphs is widely used for releasing statistics from sensitive graphs while still preserving user privacy. Most existing algorithms however are in a centralized privacy model, where a trusted data curator holds the entire graph. As this model raises a number of privacy and security issues – such as, the trustworthiness of the curator and the possibility of data breaches, it is desirable to consider algorithms in a more decentralized local model where no server holds the entire graph.

In this work, we consider a local model, and present algorithms for counting subgraphs – a fundamental task for analyzing the connection patterns in a graph – with LDP (Local Differential Privacy). For triangle counts, we present algorithms that use one and two rounds of interaction, and show that an additional round can significantly improve the utility. For k-star counts, we present an algorithm that achieves an order optimal estimation error in the non-interactive local model. We provide new lower-bounds on the estimation error for general graph statistics including triangle counts and k-star counts. Finally, we perform extensive experiments on two real datasets, and show that it is indeed possible to accurately estimate subgraph counts in the local differential privacy model.

 

Speaker:

Takao Murakami, Ph.D.

Senior Research Scientist
Cryptography Platform Research Team
Cyber Physical Security Research Center (CPSEC)
National Institute of Advanced Industrial Science and Technology (AIST)

 

Bio:

Takao Murakami is a senior research scientist at the National Institute of Advanced Industrial Science and Technology (AIST). He received the Ph.D. degree from the University of Tokyo in 2014. He was a visiting scholar at the University of California San Diego (UCSD) in 2020. His research interests include differential privacy and privacy-preserving machine learning.