Scalable non-deterministic clustering-based k-anonymization for rich networks

In this paper, we tackle the problem of graph anonymization in the context of privacy-preserving social network mining. We present a greedy and non-deterministic algorithm to achieve k -anonymity on labeled and undirected networks. Our work aims to create a scalable algorithm for real-world big netw...

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Bibliographic Details
Published in:International journal of information security Vol. 18; no. 2; pp. 219 - 238
Main Authors: Ros-Martín, Miguel, Salas, Julián, Casas-Roma, Jordi
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019
Springer Nature B.V
Subjects:
Algorithms
Anonymity
Clustering
Coding and Information Theory
Communications Engineering
Computer Communication Networks
Computer privacy
Computer Science
Cryptology
Data mining
Greedy algorithms
Machinery
Management of Computing and Information Systems
Networks
Operating Systems
Privacy
Recommender systems
Regular Contribution
Social networks
Anonymity
Privacy-preserving
Social networks
Graphs
Recommender systems
ISSN:1615-5262, 1615-5270
Online Access:Get full text
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Summary:In this paper, we tackle the problem of graph anonymization in the context of privacy-preserving social network mining. We present a greedy and non-deterministic algorithm to achieve k -anonymity on labeled and undirected networks. Our work aims to create a scalable algorithm for real-world big networks, which runs in parallel and uses biased randomization for improving the quality of the solutions. We propose new metrics that consider the utility of the clusters from a recommender system point of view. We compare our approach to SaNGreeA, a well-known state-of-the-art algorithm for k -anonymity generalization. Finally, we have performed scalability tests, with up to 160 machines within the Hadoop framework, for anonymizing a real-world dataset with around 830 K nodes and 63 M relationships, demonstrating our method’s utility and practical applicability.
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ISSN:1615-5262
1615-5270
DOI:10.1007/s10207-018-0409-1