MHD: A New Method towards Privacy Protecting Datasets Published

Fei Liu; Yan Jia; Wei Hong Han

doi:10.4028/www.scientific.net/AMM.214.792

Paper Titles

Research of Universities Teaching Materials and Resources Based on Semantic Grid Portal Construction
p.771

Research of Optimal Control Model with Multiple Inputs and Multiple Outputs Based on Logic Algebra
p.775

Eyelid Noise Detection Based on Gray-Scale Morphology and Judgement of Noise Occlusion
p.780

Application of Multivariable Non-Linear Decoupling Control Based on Neural Network
p.786

MHD: A New Method towards Privacy Protecting Datasets Published
p.792

Simulation of Large Amount of Data Liquid-Solid Coupling Based on the SPH
p.799

A Study on Component Composition Technology Based on XCM
p.804

Based on OLAP Realize Real-Time Integrated Analysis of the Underground Metal Mines Cost Information
p.809

Precise Mechanics Control and Simulation of Automotive ABS
p.817

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 214MHD: A New Method towards Privacy Protecting...

MHD: A New Method towards Privacy Protecting Datasets Published

Abstract:

In this paper, we proposed a multi-hierarchical diversity algorithm MHD to prevent privacy disclosing in dataset. We proposed some definitions of multi-hierarchical diversity firstly. Sensitive values are partitioned into several classes. We ensured no proportion of class exceeding the threshold. We generalized some values of sensitive attribute to reduce information loss. Clustering method was used to lower data distort. Greed algorithm was used to lower time cost. We compared MHD with classic algorithms, ε-cloning and m-Invariance about Time Cost, Data Distort, Usability and Imbalance. Empirical results showed that our algorithm could protect privacy and publish datasets with high security and lower information loss

You might also be interested in these eBooks

Green Power, Materials and Manufacturing Technology and Applications II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 214)

Pages:

792-798

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.214.792

Citation:

Cite this paper

Online since:

November 2012

Authors:

Fei Liu, Yan Jia, Wei Hong Han

Keywords:

Clustering, Hierarchical Diversity, K-Anonymity, Privacy Protection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Sweeney, 2002, L. k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness and Knowledge-based Systems. 10(5), (2002) 557-570.

DOI: 10.1142/s0218488502001648

Google Scholar

[2] Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M. 2006, L-diversity: Privacy beyond k-anonymity. ICDE (2006) 24-24.

DOI: 10.1109/icde.2006.1

Google Scholar

[3] Li. N, Li. T, Venkitasubramaniam, S. 2007, t-closeness: Privacy beyond k-anonymity and L-diversity. ICDE, (2007) 106-115.

Google Scholar

[4] Xiaokui Xiao, Yufei Tao. 2007, m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets. SIGMOD(2007) 689-700.

DOI: 10.1145/1247480.1247556

Google Scholar

[5] Yingyi Bu, Ada Wai-Chee Fu, Raymond Chi-Wing Wong, Lei Chen, Jiuyong Li. 2008, Privacy Preserving Serial Data Publishing By Role Composition. VLDB (2008) 1: 845-856.

DOI: 10.14778/1453856.1453948

Google Scholar

[6] Muzammil M. Baig, Jiuyong Li, Jixue Liu, Hua Wang. 2011, Cloning for Privacy Protection in Multiple Independent Data Publications. CIKM(2011) 885-894.

Google Scholar

[7] http: /ipums. org. 2010, Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek integrated public use microdata series: Version 5. 0. Minneapolis: University of Minnesota, (2010).

Google Scholar