Scalable non-deterministic clustering-based k-anonymization for rich networks View Full Text


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Article Info

DATE

2019-04

AUTHORS

Miguel Ros-Martín, Julián Salas, Jordi Casas-Roma

ABSTRACT

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. More... »

PAGES

219-238

References to SciGraph publications

  • 2012. Fast Identity Anonymization on Graphs in DATABASE AND EXPERT SYSTEMS APPLICATIONS
  • 2016. Sampling and Merging for Graph Anonymization in MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE
  • 2013-04. MIRHA: multi-start biased randomization of heuristics with adaptive local search for solving non-smooth routing problems in TOP
  • 2010-05-20. A Social Network-Based Recommender System (SNRS) in DATA MINING FOR SOCIAL NETWORK DATA
  • 2013-06. Complexity of social network anonymization in SOCIAL NETWORK ANALYSIS AND MINING
  • 2013-09. Why Waldo befriended the dummy? k-Anonymization of social networks with pseudo-nodes in SOCIAL NETWORK ANALYSIS AND MINING
  • 2007. Efficient k-Anonymization Using Clustering Techniques in ADVANCES IN DATABASES: CONCEPTS, SYSTEMS AND APPLICATIONS
  • 2015-09. Anonymizing graphs: measuring quality for clustering in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2010-01-18. A Survey of Privacy-Preservation of Graphs and Social Networks in MANAGING AND MINING GRAPH DATA
  • 2012-10. Reidentification and k-anonymity: a model for disclosure risk in graphs in SOFT COMPUTING
  • 2017-03. A survey of graph-modification techniques for privacy-preserving on networks in ARTIFICIAL INTELLIGENCE REVIEW
  • 2009. Data and Structural k-Anonymity in Social Networks in PRIVACY, SECURITY, AND TRUST IN KDD
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10207-018-0409-1

    DOI

    http://dx.doi.org/10.1007/s10207-018-0409-1

    DIMENSIONS

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