Advances in data collection and storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous amounts of data to be stored efficiently. Useful knowledge can be mined from these data, which can be used in several ways depending on the nature of the data. Quite often companies and organizations are willing to share data for the sake of mutual benefit. However, the sharing of such data comes with risks, as problems with privacy may arise. Sensitive data, along with sensitive knowledge inferred from this data, must be protected from unintentional exposure to unauthorized parties. One form of the inferred knowledge is frequent patterns mined in the form of frequent itemsets from transactional databases. The problem of protecting such patterns from being discovered, is known as the frequent itemset hiding problem. In this paper we present a toolbox, which provides several implementations of frequent itemset hiding algorithms. Firstly, we summarize the most important aspects of each algorithm. We then introduce the architecture of the toolbox and its novel features. Finally, we provide experimental results on real world datasets, demonstrating the capabilities of the toolbox and the convenience it offers in comparing different algorithms.
Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements. In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed. Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed in this context in order to extract this information in the most efficient way. However efficiency is not our only concern in this study. The security and privacy issues over the extracted knowledge must be seriously considered as well. By taking this into consideration, we study the discovery of association rules in binary data sets and we propose algorithms for selectively hiding sensitive association rules. Association rule hiding is a well researched area in privacy preserving data mining and many algorithms have been proposed to address it. The algorithms that we introduce use a distortion-based technique for hiding the sensitive rules. The hiding process may introduce a number of side effects either by generating rules which were not previously existing (ghost rules) or by eliminating existing non-sensitive rules (lost rules). The proposed algorithms use effective data structures for the representation of the association rules and they strongly rely on the prioritization of the selection of the transactions to choose for falsification (victim transactions) by using weights. In this paper we show that our algorithms perform better than other similar algorithms in this field in eliminating non-sensitive rules without increasing the processing time significantly. Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. We investigate confidentiality issues of a broad category of rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database.