Privacy preservation in data mining is a pioneering research area as the security of increasing amount of data is under risks. Privacy preservation in Data Mining [PPDM] is a delicate task as there is a trade-off between data Anonymization and their utility. Existing PPDM techniques uses Anonymization using randomization, generalization or suppression which reduces the utility of data. They als...

The pervasiveness of location-aware devices has spawned extensive research in trajectory data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing trajectory data among different parties often creates an obstacle for effective data mining. In this paper, we study the challenges of anonymizing trajectory data: high dimensionality, sparseness, and sequenti...

Data mining plays a vital role in today’s information world wherein it has been widely applied in various business organizations. The current trend in business collaboration demands the need to share data or mined results to gain mutual benefit. However it has also raised a potential threat of revealing sensitive information when releasing data. Data sanitization is the process to conceal the s...

Utility episode mining has emerged as an interesting and challenging research topic in data mining. It finds applications in anomaly detection, biomedical data analysis, predicting stock trends etc. The number of high-utility episodes that can be extracted from a sequence depends upon the value of minimum utility threshold. It is often difficult for a user to find a suitable threshold value whi...

Recently there has been a growing interest in designing differentially private data mining algorithms. A variety of algorithms have been proposed for mining frequent itemsets. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. It has practical importance in a wide range of application areas such as decision support, web usage mining, bioinformatics, etc. In th...

Online social networks and publication of social network data has led to the risk of leakage of confidential information of individuals. This requires the preservation of privacy before such network data is published by service providers. Privacy in online social networks data has been of utmost concern in recent years. Hence, the research in this field is still in its early years. Several publ...

In this paper, a new measure, namely maximum utility measure, is designed to evaluate the utility of a pattern in a sequence. Based on the measure, a projection-based algorithm for finding high-utility sequential patterns from a sequence database is developed. Experimental results show the performance of the proposed algorithm as well.

High-utility itemset mining (HUIM) is an important research topic in data mining field and extensive algorithms have been proposed. However, existing methods for HUIM present too many high-utility itemsets (HUIs), which reduces not only efficiency but also effectiveness of mining since users have to sift through a large number of HUIs to find useful ones. Recently a new representation, closed +...