tau -Safe ( l,k )-Diversity Privacy Model for Sequential Publication With High Utility

• Published in: IEEE access Vol. 7; pp. 687 - 701
• Main Authors: Zhu, Hui, Liang, Hong-Bin, Zhao, Lian, Peng, Dai-Yuan, Xiong, Ling
• Format: Journal Article
• Language: English
• Published: IEEE 2019
• Subjects: (<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l , <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k )-diversity; <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k )-diversity; <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l; anonymization; bucketization; Data models; Data privacy; Diseases; Privacy; Publishing; Resists; Safety; Sequential publication
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2885618

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>-safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>-safe (<inline-formula> <tex-math notation="LaTeX">l,k </tex-math></inline-formula>)-diversity privacy model based on generalization and segmentation by record anonymity satisfying <inline-formula> <tex-math notation="LaTeX">l </tex-math></inline-formula>-diversity and individual anonymity satisfying <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-anonymity. This privacy model ensures that each record's signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula>-invariance and <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>-safety model in the sequential publication with arbitrary updates.