A Multi-Objective Degree-Based Network Anonymization Approach
Ola N. Halawi, Faisal N. Abu-Khzam

TL;DR
This paper introduces a multi-objective degree-based network anonymization method that enhances privacy while maintaining network utility, using integer linear programming to optimize degree modifications under local constraints.
Contribution
It generalizes degree anonymization to a multi-objective model with local restrictions, solved via integer linear programming for improved privacy and utility balance.
Findings
Negligible impact on node clustering accuracy
Significant improvement in data privacy
Effective optimization with integer linear programming
Abstract
Enormous amounts of data collected from social networks or other online platforms are being published for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have motivated the work on degree-based data anonymization. We propose and study a new multi-objective anonymization approach that generalizes the known degree anonymization problem and attempts at improving it as a more realistic model for data security/privacy. Our suggested model guarantees a convenient privacy level based on modifying the degrees in a way that respects some given local restrictions, per node, such that the total modifications at the global level (in the whole graph/network) are bounded by some given value. The corresponding multi-objective graph realization approach is solved using Integer Linear Programming to obtain the best possible…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Internet Traffic Analysis and Secure E-voting
