An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

AbstractAt present, most of privacy preserving approaches in data publishing are applied to single sensitive attribute. However, applying single-sensitive-attribute privacy preserving techniques directly into data with multiple sensitive attributes often causes leakage of large amount of private information. This paper focuses on the privacy preserving methods in data publishing for multiple sensitive attributes. It combines data anonymous methods based on lossy join with the idea of clustering. And it proposes an improved algorithm of individuation K-anonymity for multiple sensitive attributes—$$ MSA(\alpha ,l) $$MSA(α,l) algorithm. By setting parameters $$ \alpha $$α and $$ l $$l, it can restrain sensitive attribute values in equivalence class, to make a more balanced distribution of sensitive attributes and satisfy the demand of diversity, then this algorithm is applied to K-anonymity model. Finally, the result of experiment shows that this improved model can preserve the privacy of sensitive data, and it can also reduce the information hidden rate.

[1]  Pawan Patidar,et al.  K-AMOA: K-Anonymity Model for Multiple Overlapped Attributes , 2016, ICTCS '16.

[2]  Feng Li,et al.  Privacy Preservation in Database Applications: A Survey: Privacy Preservation in Database Applications: A Survey , 2009 .

[3]  Zhang Yong,et al.  A Privacy-Preserving Data Publishing Algorithm for Clustering Application , 2010 .

[4]  Wang Ji-yi.  An Improved Semi-Supervised K-Means Clustering Algorithm , 2011 .

[5]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[6]  Kechao Wang,et al.  Anatomy: Uncertain Data k-Anonymity Privacy Protection Algorithm , 2013 .

[7]  Xie Jin.  A Privacy Preserving Approach Based on Attributes Correlation Partition for Multiple Sensitive Attributes , 2014 .

[8]  Yufei Tao,et al.  Anatomy: simple and effective privacy preservation , 2006, VLDB.

[9]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Jia Jiong,et al.  A Multi-Level l-Diversity Model for Numerical Sensitive Attributes , 2011 .

[11]  Yang Xiao.  Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing , 2008 .

[12]  Hao Yan,et al.  The improvement and application of a K-means clustering algorithm , 2016, 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

[13]  Liu Xiang,et al.  Survey on Privacy Preserving Techniques for Publishing Social Network Data , 2014 .

[14]  Lihong Wang,et al.  K*-Means: An Effective and Efficient K-Means Clustering Algorithm , 2016, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom).

[15]  Shunxiang Zhang,et al.  An enhanced l-diversity privacy preservation , 2013, 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[16]  Katsumi Takahashi,et al.  k-Anonymous Microdata Release via Post Randomisation Method , 2015, IWSEC.

[17]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[18]  Josep Domingo-Ferrer,et al.  From t-closeness to differential privacy and vice versa in data anonymization , 2015, Knowl. Based Syst..

[19]  Jia Jiong.  Individuation Privacy Preservation Oriented to Sensitive Values , 2010 .

[20]  Emad Elabd,et al.  Semantic anonymization in publishing categorical sensitive attributes , 2016, 2016 8th International Conference on Knowledge and Smart Technology (KST).

[21]  Yong Xu,et al.  K-Means Clustering Algorithm with Refined Initial Center , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[22]  Ji-Gui Sun,et al.  Clustering Algorithms Research , 2008 .

[23]  Huan Liu,et al.  Topic taxonomy adaptation for group profiling , 2008, TKDD.

[24]  Josep Domingo-Ferrer,et al.  A k-anonymous approach to privacy preserving collaborative filtering , 2015, J. Comput. Syst. Sci..

[25]  Jan Willemson,et al.  Privacy Protection for Wireless Medical Sensor Data , 2016, IEEE Transactions on Dependable and Secure Computing.

[26]  Yong Xiang,et al.  Protection of Big Data Privacy , 2016, IEEE Access.

[27]  Ge Yu,et al.  Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing: Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing , 2009 .

[28]  Zhou Shui.  Privacy Preservation in Database Applications:A Survey , 2009 .