A Crypto-Assisted Approach for Publishing Graph Statistics with Node Local Differential Privacy

by   Shang Liu, et al.

Publishing graph statistics under node differential privacy has attracted much attention since it provides a stronger privacy guarantee than edge differential privacy. Existing works related to node differential privacy assume a trusted data curator who holds the whole graph. However, in many applications, a trusted curator is usually not available due to privacy and security issues. In this paper, for the first time, we investigate the problem of publishing the graph degree distribution under Node Local Differential privacy (Node-LDP), which does not rely on a trusted server. We propose an algorithm to publish the degree distribution with Node-LDP by exploring how to select the optimal graph projection parameter and how to execute the local graph projection. Specifically, we propose a Crypto-assisted local projection method that combines LDP and cryptographic primitives, achieving higher accuracy than our baseline PureLDP local projection method. On the other hand, we improve our baseline Node-level parameter selection by proposing an Edge-level parameter selection that preserves more neighboring information and provides better utility. Finally, extensive experiments on real-world graphs show that Edge-level local projection provides higher accuracy than Node-level local projection, and Crypto-assisted parameter selection owns the better utility than PureLDP parameter selection, improving by up to 79.8 respectively.


Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach

Graph Neural Networks have achieved tremendous success in modeling compl...

Metric-based local differential privacy for statistical applications

Local differential privacy (LPD) is a distributed variant of differentia...

Locally Differentially Private Analysis of Graph Statistics

Differentially private analysis of graphs is widely used for releasing s...

Multi-Central Differential Privacy

Differential privacy is typically studied in the central model where a t...

Quantifying Surveillance in the Networked Age: Node-based Intrusions and Group Privacy

From the "right to be left alone" to the "right to selective disclosure"...

DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications

Differential privacy (DP) has arisen as the state-of-the-art metric for ...

Please sign up or login with your details

Forgot password? Click here to reset