Publishing transactional data about individuals in an anonymous form is increasingly required by organizations. Recent approaches ensure that potentially identifying information cannot be used to link published transactions to individuals’ identities. However, these approaches are inadequate to anonymize data that is both protected and practically useful in applications because they incorporate coarse privacy requirements, do not integrate utility requirements, and tend to explore a small portion of the solution space. In this paper, we propose the first approach for anonymizing transactional data under application-specific privacy and utility requirements. We model such requirements as constraints, investigate how these constraints can be specified, and propose COnstraint-based Anonymization of Transactions, an algorithm that anonymizes transactions using a flexible anonymization scheme to meet the specified constraints. Experiments with benchmark datasets verify that COAT significantly outperforms the current state-of-the-art algorithm in terms of data utility, while being comparable in terms of efficiency. Our approach is also shown to be effective in preserving both privacy and utility in a real-world scenario that requires disseminating patients’ information.

COAT: COnstraint-based Anonymization of Transactions

Support vector machine (SVM) constructs an optimal hyperplane utilizing a small set of vectors near boundary. The proximal SVM is an extremely simple procedure to generate linear and nonlinear classifier based on proximity to one of two parallel planes that are separated as far as possible. However, when the two-class are very unbalanced, the proximal SVM tends to fit better the class with more samples and has higher error in fewer samples. Further more, this draw back exists in K-category classification by using one-from-the-rest (OFR) separation for each class. To solve the problem, an improved SVM algorithm is presented in this paper. Experimental results show that the novel approach is prior to the proximal SVM.

A classification algorithm for multi-classes based on SVM

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A classification algorithm for multi-classes based on SVM