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Order Dataset Release Scheme Based on Safe K-Anonymization for Privacy Protection in Cloud Manufacturing

[+] Author Affiliations
Hui Xiu, Xuemei Jiang, Xiaomei Zhang

Wuhan University of Technology, Wuhan, China

Paper No. MSEC2017-2720, pp. V003T04A031; 7 pages
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 3: Manufacturing Equipment and Systems
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5074-9
  • Copyright © 2017 by ASME


Cloud Manufacturing is a new model to increase the manufacturing and business benefits by sharing manufacturing resources. These resources can bring users convenience, but also may be maliciously analyzed by the attacker which may result in personal or corporate privacy disclosure. In this paper, we discuss the privacy disclosure problem in cloud manufacturing, and propose a method for releasing order data securely with the complex relationship between enterprises and other vendors. With regards to the risk of privacy leakage in the process of data analysis or data mining, we improve the traditional method of anonymous releasing for original order data, and introduce the thought of safe k-anonymization to achieve the process. To meet the needs of protecting sensitive information in data, we analyze the users’ different demands for order data in the cloud manufacturing, use the sampling function to satisfy (β, ε, δ) - DPS to increase the uncertainty of the differential privacy, improve the k-anonymization method, apply the anonymous method with generalization, concealment, and reduce data associations to different attributes. The improved method not only preserves the statistical characteristics of the data, but also protects the privacy information in the order data in the cloud manufacturing environment.

Copyright © 2017 by ASME
Topics: Manufacturing



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