Anonimos: An LP based Approach for Anonymizing Weighted Social Network Graphs
Sudipto Das, Omer Egecioglu, Amr El Abbadi

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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Internet Traffic Analysis and Secure E-voting · Privacy, Security, and Data Protection
