Efficient Multidimensional Suppression for K-Anonymity

Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being released for data mining. One way to anonymize data set is to manipulate its content so that the records adhere to k-anonymity. Two common manipulation techniques used to achieve k-anonymity of a data set are generalization and suppression. Generalization refers to replacing a value with a less specific but semantically consistent value, while suppression refers to not releasing a value at all. Generalization is more commonly applied in this domain since suppression may dramatically reduce the quality of the data mining results if not properly used. However, generalization presents a major drawback as it requires a manually generated domain hierarchy taxonomy for every quasi-identifier in the data set on which k-anonymity has to be performed. In this paper, we propose a new method for achieving k-anonymity named K-anonymity of Classification Trees Using Suppression (kACTUS). In kACTUS, efficient multidimensional suppression is performed, i.e., values are suppressed only on certain records depending on other attribute values, without the need for manually produced domain hierarchy trees. Thus, in kACTUS, we identify attributes that have less influence on the classification of the data records and suppress them if needed in order to comply with k-anonymity. The kACTUS method was evaluated on 10 separate data sets to evaluate its accuracy as compared to other k-anonymity generalization- and suppression-based methods. Encouraging results suggest that kACTUS' predictive performance is better than that of existing k-anonymity algorithms. Specifically, on average, the accuracies of TDS, TDR, and kADET are lower than kACTUS in 3.5, 3.3, and 1.9 percent, respectively, despite their usage of manually defined domain trees. The accuracy gap is increased to 5.3, 4.3, and 3.1 percent, respectively, when no domain trees are used.

1 Introduction

Knowledge Discovery in Databases (KDDs) is the process of identifying valid, novel, useful, and understandable patterns from large data sets. Data Mining (DM) is the core of the KDD process, involving algorithms that explore the data, develop models, and discover significant patterns. Data mining has emerged as a key tool for a wide variety of applications, ranging from national security to market analysis. Many of these applications involve mining data that include private and sensitive information about users [1]. For instance, medical research might be conducted by applying data mining algorithms on patient medical records to identify disease patterns. A common practice is to deidentify data before releasing it and applying a data mining process in order to preserve the privacy of users. However, private information about users might be exposed when linking deidentified data with external public sources. For example, the identity of a 95-year-old patient may be inferred from deidentified data that include the patients' addresses, if she is known as the only patient at this age in her neighborhood. This is true even if sensitive details such as her social security number, her name, and the name of the street, where she lives, were omitted.