We examine Euclidean distance-preserving data perturbation as a tool for privacy-preserving data mining. Such perturbations allow many important data mining algorithms e.g. hierarchical and k-means clustering), with only minor modification, to be applied to the perturbed data and produce exactly the same results as if applied to the original data. However, the issue of how well the privacy of the original data is preserved needs careful study. We engage in this study by assuming the role of an attacker armed with a small set of known original data tuples (inputs). Little work has been done examining this kind of attack when the number of known original tuples is less than the number of data dimensions. We focus on this important case, develop and rigorously analyze an attack that utilizes any number of known original tuples. The approach allows the attacker to estimate the original data tuple associated with each perturbed tuple and calculate the probability that the estimation results in a privacy breach. On a real 16-dimensional dataset, we show that the attacker, with 4 known original tuples, can estimate an original unknown tuple with less than 7 error with probability exceeding 0.8.
Evfimievski @cite_11 , Rizvi and Haritza @cite_8 considered the use of categorical data perturbation in the context of association rule mining. Their algorithms delete real items and add bogus items to the original records. Association rules present in the original data can be estimated from the perturbed data. Along a related line, Verykios @cite_28 considered perturbation techniques which allow the discovery of some association rules while hiding others considered to be sensitive.
@cite_28 @cite_8 @cite_11
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The discovery of association rules from large databases has proven beneficial for companies since such rules can be very effective in revealing actionable knowledge that leads to strategic decisions. In tandem with this benefit, association rule mining can also pose a threat to privacy protection. The main problem is that from non-sensitive information or unclassified data, one is able to infer sensitive information, including personal information, facts, or even patterns that are not supposed to be disclosed. This scenario reveals a pressing need for techniques that ensure privacy protection, while facilitating proper information accuracy and mining. In this paper, we introduce new algorithms for balancing privacy and knowledge discovery in association rule mining. We show that our algorithms require only two scans, regardless of the database size and the number of restrictive association rules that must be protected. Our performance study compares the effectiveness and scalability of the proposed algorithms and analyzes the fraction of association rules, which are preserved after sanitizing a database. We also report the main results of our performance evaluation and discuss some open research issues. 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. Due to growing concerns about the privacy of personal information, organizations that use their customers' records in data mining activities are forced to take actions to protect the privacy of the individuals. A frequently used disclosure protection method is data perturbation. When used for data mining, it is desirable that perturbation preserves statistical relationships between attributes, while providing adequate protection for individual confidential data. To achieve this goal, we propose a kd-tree based perturbation method, which recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The confidential data in each final subset are then perturbed using the subset average. An experimental study is conducted to show the effectiveness of the proposed method Finding interesting association rules is an important and active research field in data mining. The algorithms of the Apriori family are based on two rule extraction measures, support and confidence. Although these two measures have the virtue of being algorithmically fast, they generate a prohibitive number of rules most of which are redundant and irrelevant. It is therefore necessary to use further measures which filter uninteresting rules. Many synthesis studies were then realized on the interestingness measures according to several points of view. Different reported studies have been carried out to identify "good" properties of rule extraction measures and these properties have been assessed on 61 measures. The purpose of this paper is twofold. First to extend the number of the measures and properties to be studied, in addition to the formalization of the properties proposed in the literature. Second, in the light of this formal study, to categorize the studied measures. This paper leads then to identify categories of measures in order to help the users to efficiently select an appropriate measure by choosing one or more measure(s) during the knowledge extraction process. The properties evaluation on the 61 measures has enabled us to identify 7 classes of measures, classes that we obtained using two different clustering techniques.