Improving Privacy in Graphs Through Node Addition

TL;DR

This paper introduces a novel privacy-preserving method for graphs by adding fake nodes, achieving $k$-anonymity against seed-based and degree attacks without significantly altering key graph properties.

Contribution

The paper proposes a new approach using fake node addition to ensure $k$-anonymity against strong attacks, addressing limitations of edge-based methods.

Findings

Achieves $k$-anonymity against seed-based attacks.

Ensures uniform expected node degree for privacy.

Maintains key graph properties with minimal structural change.

Abstract

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users' identity. Since structure-based de-anonymization attacks can reveal users' identity's even when the graph is simply anonymized by employing naive ID removal, recently, $k-$anonymity is proposed to secure users' privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides $k-$anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have $k-$anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.