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A Privacy-Aware Access Model on Anonymized Data

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Trusted Systems (INTRUST 2014)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9473))

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Abstract

With development of information technology and communication, corporations and individuals will collect some digital information to support information-based decisions. However, under some conditions, if all original data are released, some privacy will be disclosed, which will threaten data security and data privacy. Therefore, data owners will take some security measures. Role-based access control may authorize related original data accessed by users according to their roles. Privacy-preserving technology release processed data to avoid privacy disclosure. Nevertheless, existing privacy-preserving technologies lack continuity and are quite inefficient. This paper establishes an access model about on anonymized data and combines with the foregoing two security measures. On the premise that data security and data privacy are ensured, there is more flexibility and diversity and work efficiency is improved as well.

Project was partially supported by Research Fund for the Doctoral Program of Higher Education of China (No. 20120009110007), Program for Innovative Research Team in University of Ministry of Education of China (No. IRT201206) and Program for New Century Excellent Talents in University (NCET-11-0565).

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Xuezhen Huang, Jiqiang Liu & Zhen Han

Authors
  1. Xuezhen Huang
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  2. Jiqiang Liu
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  3. Zhen Han
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Corresponding author

Correspondence to Xuezhen Huang .

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Editors and Affiliations

  1. Google, New York, New York, USA

    Moti Yung

  2. Beijing Institute of Technology, Beijing, China

    Liehuang Zhu

  3. Institute for Infocomm Research, Singapore

    Yanjiang Yang

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Huang, X., Liu, J., Han, Z. (2015). A Privacy-Aware Access Model on Anonymized Data. In: Yung, M., Zhu, L., Yang, Y. (eds) Trusted Systems. INTRUST 2014. Lecture Notes in Computer Science(), vol 9473. Springer, Cham. https://doi.org/10.1007/978-3-319-27998-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-27998-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27997-8

  • Online ISBN: 978-3-319-27998-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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