Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework

Abstract

As more and more person-specific data like health information becomes available, increasing attention is paid to confidentiality and privacy protection. One proposed model of privacy protection is k- Anonymity, where a dataset is k-anonymous if each record is identical to at least (k-1) others in the dataset. Our goal is to minimize information loss while transforming a collection of records to satisfy the k-Anonymity model. The downside to current greedy anonymization algorithms is their potential to get stuck at poor local optimums. In this paper, we propose an Ordered Greed Framework for k-Anonymity. Using our framework, designers can avoid the poor-local-optimum problem by adding stochastic elements to their greedy algorithms. Our preliminary experimental results indicate improvements in both runtime and solution quality. We also discover a surprising result concerning at least two widely-accepted greedy optimization algorithms in the literature. More specifically, for anonymization algorithms that process datasets in column-wise order, we show that a random column ordering can lead to significantly higher quality solutions than orderings determined by known greedy heuristics.