Efficient k-Anonymization Using Clustering Techniques

Abstract

Abstract. k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. The key observation here is that data records that are naturally similar to each other should be part of the same equivalence class. We thus formulate a specific clustering problem, referred to as k-member clustering problem. We prove that this problem is NP-hard and present a greedy heuristic, the complexity of which is in O(n 2 ). As part of our approach we develop a suitable metric to estimate the information loss introduced by generalizations, which works for both numeric and categorical data.

Graph Based Local Recoding for Data Anonymization

“…Most of the local recoding generalization algorithms follow clustering based approach where each cluster should satisfy anonymity requirement [1,2,6,10,14,19,28]. [2] Proposed condensation based approach where the data is condensed into multiple groups having pre-defined size.…”

“…The key idea of this algorithm is to generate the equivalence classes based on density and is measured by k-nearest neighbour distance. Ji-Won Byun et al formulated greedy approach in which kanonymity problem is transformed into k-member clustering to attaining the privacy protection of the data [6]. A frame work called KACA to accomplish the k-anonymity,in which grouping of the tuples is done by attribute hierarchical structures [21].…”

A Privacy Protection Method for Social Network Data against Content/Degree Attacks

“…To measure the loss of content information, we adopt the measurement introduced in [5]. The content information of a node consists of numerical attributes and hierarchical attributes.…”

“…Zheleva et al [15] proposed an edge clustering method which considers the content information attached to edges, but this method also causes the same problem as [5]. Wei et al [13] suggested a method that produces unconnected social networks.…”

L-Diversity Based Dynamic Update for Large Time-Evolving Microdata

“…We applied the cluster technique reported in [4]. To ensure that l-diversity is correctly enforced, two constraints are required when the clustering process is performed.…”