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International Conference on Intelligent Networking and Collaborative Systems

INCoS 2017: Advances in Intelligent Networking and Collaborative Systems pp 491–502Cite as

Privacy Preserving Data Mining on Big Data Computing Platform: Trends and Future

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

Data mining is becoming increasingly important in the data-driven society in recent years. Unfortunately, privacy of the individuals fails to be protected and considered deliberately. It’s a significantly challenging question that outputs of data mining models can be applied to preserve privacy while simultaneously maintaining analyzing capability. With advancements in big data, series of big data computing platforms have evolved into widely utilized paradigms for data mining. However, users’ sensitive data which are outsourced on the cloud and mined on open-sourced computing platform. It poses such severe threats that measures must be taken to protect the privacy of individuals’ data. Regarding this issue, much fruitful work has been done on designing privacy preserving data mining approaches for improving big data computing platform security and privacy of individuals. In this paper, a systematic investigation of a wide array of the state-of-the-art privacy preserving data mining (PPDM) techniques has been performed from different aspects on threat model, anonymity, secure multiparty computation (SMC), differential privacy. We are focused on improving data privacy in these sensitive areas on big data computing platforms. Hopefully, our work aims to highlight the urgent need for applying privacy preserving data mining approaches on big data computing platforms. Moreover, a better understanding of this research area may benefit the usage of big data and future exploration.

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Acknowledgments

We will thank for National Natural Science Foundation funded project 61309008 and Shaanxi Province Natural Science Funded Project 2014JQ8049. Also, we would also like to thank our partners at our Research Lab and their generous gifts in support of this research.

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Authors and Affiliations

  1. Department of Information Engineering, Engineering University of PAP, Xi’an, Shaanxi, China

    Gao Zhiqiang & Zhang Longjun

Authors
  1. Gao Zhiqiang
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  2. Zhang Longjun
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Corresponding author

Correspondence to Gao Zhiqiang .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

  3. School of Business, UNSW Australia, Canberra, Aust Capital Terr, Australia

    Omar Khadeer Hussain

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Zhiqiang, G., Longjun, Z. (2018). Privacy Preserving Data Mining on Big Data Computing Platform: Trends and Future. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_44

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  • DOI: https://doi.org/10.1007/978-3-319-65636-6_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

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