Anonimos: An LP based Approach for Anonymizing Weighted Social Network Graphs

TL;DR

This paper introduces Anónimos, a linear programming method for anonymizing weighted social network graphs by protecting edge weights while preserving key linear properties crucial for graph algorithms.

Contribution

It presents a novel LP-based approach for edge weight anonymization that maintains linear graph properties, enabling secure sharing of social network data.

Findings

Successfully anonymizes edge weights in real social networks

Enhances k-anonymity and scrambles weight orderings for robustness

Preserves multiple linear properties simultaneously

Abstract

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present An\'onimos, a Linear Programming based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply An\'onimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that An\'onimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. Additionally, we demonstrate the composability of different models generated using An\'onimos, a property that allows a single anonymized graph to preserve multiple linear properties.