Information Security in Big Data: Privacy and Data Mining
By Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, Yong Ren. IEEE Access, 2014
598 citations recorded by the source corpus.
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 privacy-preserving 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.
Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/619f3629a91e20d9916331874f7686cd304a62ce
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hayat Khaloufi
- Abderrahim Beni Hssane
- Karim Abouelmehdi
- Nilay Khare
- Manasi Gyanchandani
- Priyank Jain
In Sociology and Political Science
- Hayat Khaloufi
- Abderrahim Beni Hssane
- Nilay Khare
- Manasi Gyanchandani
- Priyank Jain
- Karim Abouelmehdi
Citations in the corpus
- Big data privacy: a technological perspective and review (2016, background): “By active data generation, we mean that the data owner will give the data to a third party [17], while passive data generation refers to the circumstances that the data are produced by data owner’s online actions (e.”
- Big healthcare data: preserving security and privacy (2018, methodology): “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.”