The pervasive impact of business computing has made information technology an indispensable part of daily operations and the key to success for enterprises. Data mining, as one of the IT services most needed by enterprises, has been realized as an important way for discovering knowledge from the data and converting “data rich” to “knowledge rich” so as to assist strategic decision making. The benefits of using data mining for decision making have been demonstrated in various industries and governmental sectors, e.g., banking, insurance, direct-mail marketing, telecommunications, retails, and health care [8,14,22]. Among all the available data mining methods, the discovery of associations between business events or transactions is one of the most commonly used data mining techniques. Association rule mining has been an important application in decision support and marketing strategy [19] for an enterprise.
At the same time, many enterprises have accumulated large amount of data from various channels in today's digitalized age. It is important to make these data available for decision making. Enterprise data mining provides such a technique for the exploration and analysis of data so as to reveal hidden information and knowledge. These processes involve extensive collaborations (e.g., exchange or sharing of business data) across different divisions of an enterprise or even enterprises themselves. However, there is also a security concern of potential risk of exposing privacy (and losing business intelligence) of an enterprise during the practice [20]. This is because either the data or the revealed information may contain the privacy of an enterprise. During the data analysis process, e.g., data mining, data transferring, and data sharing, it involves some elements containing sensitive information from which an adversary can decipher the privacy of an enterprise. Without proper security policy and technology, enterprise privacy could be very vulnerable to security breaches. Therefore, it is urgent and critical to provide solutions to protecting enterprise privacy for data mining in different application scenarios.
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Chiew, K. (2008). Data Mining with Privacy Preserving in Industrial Systems. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_3
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