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Transactional data collection and sharing currently face the challenge of how to prevent information leakage and protect data from privacy breaches while maintaining high-quality data utilities. Data anonymization methods such as perturbation, generalization, and suppression have been proposed for privacy protection. However, many of these methods incur excessive information loss and cannot satisfy...
Transactional data with attributes of multiple types may be extremely useful to secondary analysis (e.g., learning models and finding patterns). However, anonymization of such data is challenging because it contains multiple types of attributes (e.g., relational and set-valued attributes). Existing privacy-preserving techniques are not applicable to address this problem. In this paper, we propose...
Data collection by social networking applications offers many opportunities for mining information, which provides a better understanding of social structures and their dynamic structures. Anonymization of social networks before they are published or shared is particularly important, since social network data usually contain much sensitive information on individuals. In this paper, we address the...
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