Data-Oblivious Graph Drawing Model and Algorithms

TL;DR

This paper introduces a data-oblivious graph drawing model suitable for cloud computing, enabling privacy-preserving visualization algorithms that operate efficiently with minimal local storage.

Contribution

It proposes a novel framework for graph drawing that maintains data privacy in cloud environments and demonstrates how classic algorithms can be adapted to this setting.

Findings

Efficient implementation of graph drawing algorithms in a data-oblivious framework.

Preservation of privacy during graph visualization in cloud computing.

Framework applicable to various classic graph drawing algorithms.

Abstract

We study graph drawing in a cloud-computing context where data is stored externally and processed using a small local working storage. We show that a number of classic graph drawing algorithms can be efficiently implemented in such a framework where the client can maintain privacy while constructing a drawing of her graph.