Contrained Generalization For Data Anonymization - A Systematic Search Based Approach
Bijit Hore, Ravi Jammalamadaka, Sharad Mehrotra, Amedeo D'Ascanio

TL;DR
This paper introduces a systematic enumeration-based approach for data generalization in anonymization, optimizing privacy and utility constraints more effectively than heuristic methods.
Contribution
It develops a complete enumeration framework with pruning heuristics for globally optimal data generalization under multiple privacy constraints.
Findings
Outperforms greedy algorithms in finding optimal solutions.
Handles multiple complex privacy constraints simultaneously.
Demonstrates effectiveness through extensive experiments.
Abstract
Data generalization is a powerful technique for sanitizing multi-attribute data for publication. In a multidimensional model, a subset of attributes called the quasi-identifiers (QI) are used to define the space and a generalization scheme corresponds to a partitioning of the data space. The process of sanitization can be modeled as a constrained optimization problem where the information loss metric is to be minimized while ensuring that the privacy criteria are enforced. The privacy requirements translate into constraints on the partitions (bins), like minimum occupancy constraints for k-anonymity, value diversity constraint for l-diversity etc. Most algorithms proposed till date use some greedy search heuristic to search for a locally optimal generalization scheme. The performance of such algorithms degrade rapidly as the constraints are made more complex and numerous. To address…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Complexity and Algorithms in Graphs
