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The effect of distant connections on node anonymity in complex networks
Rachel G de Jong1,2, Mark P J van der Loo3,4, Frank W Takes3
1Leiden University, LIACS, 2333 CA, Leiden, The Netherlands. r.g.de.jong@liacs.leidenuniv.nl.
Scientific Reports
|January 11, 2024
Summary
New methods, d-k-anonymity and anonymity-cascade, improve privacy assessments in social network analysis. Even distant connections significantly reduce anonymity, impacting data sharing strategies.
Area of Science:
- Computer Science
- Social Network Analysis
- Cybersecurity
Background:
- Individual privacy is crucial in social network analysis research.
- Existing anonymity measures based on ego networks are insufficient.
- Distant connections pose significant de-anonymization risks.
Purpose of the Study:
- To develop novel metrics for assessing network anonymity.
- To account for the impact of distant connections on privacy.
- To provide flexible solutions for large-scale network analysis.
Main Methods:
- Introduction of d-k-anonymity, considering information up to distance d.
- Development of anonymity-cascade to capture the "infectiousness" of uniqueness.
- Incorporation of "twin node" processing for graph structures.
Main Results:
- Experiments show significant anonymity reduction when connections at distance 2 are considered.
- Extending analysis beyond the ego network decreases anonymity by over 50% with one extra link.
- Proposed methods are tunable for precision and computation time, enabling large-scale network analysis.
Conclusions:
- Traditional ego network-based anonymity measures are inadequate.
- Distant connections drastically impact individual privacy in networks.
- Findings necessitate revised strategies for privacy-aware sharing of sensitive network data.
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