Information Security in Big Data: Privacy and Data Mining

Abstract

The growing popularity and development of data mining technologies bring serious threat to the security of individual's sensitive information. An emerging research topic in data mining, known as privacypreserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Big healthcare data: preserving security and privacy

“…This lifecycle model is continually being improved with emphasis on constant attention and continual monitoring [21]. In this paper, we suggest a model that combines the phases presented in [20] and phases mentioned in [21], in order to provide encompass policies and mechanisms that ensure addressing threats and attacks in each step of big data life cycle. Figure 1 presents the main elements in big data lifecycle in healthcare.…”

“…Yazan et al [20] suggested a big data security lifecycle model extended from Xu et al [21]. This model is designed to address the phases of the big data lifecycle and correlate threats and attacks that face big data environment within these phases, while [21] address big data lifecycle from user role perspective: data provider, data collector, data miner, and decision maker.…”

“…This model is designed to address the phases of the big data lifecycle and correlate threats and attacks that face big data environment within these phases, while [21] address big data lifecycle from user role perspective: data provider, data collector, data miner, and decision maker. The model proposed in [20] comprised of four interconnecting phases: data collection phase, data storage phase, data processing and analysis, and knowledge creation.…”

A Study Towards Post Hoc Forensic Analysis Using Big Data Analytics

“…The privacy issues are viewed in a wider perspective when it relates to data mining and considers various approaches to protect sensitive information [19]. A new knowledge can be extracted through data analytics and aggregated through data processing but not lead to privacy issues of a user [14].…”

“…Data provenance in big data has been explored recently but it has been studied in database and distributed systems communitie's. It simply refers to the ownership of data or custodian of data and the location of the data where it resides [19]. It is a process of maintaining the origin and creation of data which is useful for validating the data, determining the reliability of data and evaluating the quality of data.…”