ICTACT Journal on Soft Computing

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Privacy Preservation of Micro Data Publishing using Fragmentation


Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, India
2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, India
3 Department of Computing Science and Engineering, Galgotias University, India
     

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Organization such as hospitals, publish detailed data or micro data about individuals for research or statistical purposes. Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. When releasing the micro data, it is necessary to prevent the sensitive information of the individuals from being disclosed. Several existing privacy-preserving approaches focus on anonymization techniques such as generalization and bucketization. Recent work has shown that generalization loses considerable amount of information for high dimensional data, the bucketization does not prevent membership disclosure and does not make clear separation between quasi-identifying attributes and sensitive attributes. In this work a novel technique called Fragmentation is proposed for publishing sensitive data with preventing the sensitive information of the individual. Here first the vertical Fragmentation is applied to attributes. In vertical Fragmentation, attributes are segmented into columns. Each column contains a subset of attributes. Secondly, the horizontal Fragmentation is applied to tuples. In this, tuples are segmented into buckets. Each bucket contains a subset of tuples. Finally the real dataset is used for experiments and the results show that this Fragmentation technique preserves better utility while protecting privacy threats and prevents the membership disclosure.

Keywords

Privacy, Privacy Preservation, Data Anonymization, Data Publishing, Data Security.
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  • Privacy Preservation of Micro Data Publishing using Fragmentation

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Authors

V. Arul
Department of Computer Science and Engineering, Anna University, Chennai, India
C. Vairavel
Department of Computer Science and Engineering, Anna University, Chennai, India
M. Prakash
Department of Computer Science and Engineering, SRM Institute of Science and Technology, India
N. V. Kousik
Department of Computing Science and Engineering, Galgotias University, India

Abstract


Organization such as hospitals, publish detailed data or micro data about individuals for research or statistical purposes. Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. When releasing the micro data, it is necessary to prevent the sensitive information of the individuals from being disclosed. Several existing privacy-preserving approaches focus on anonymization techniques such as generalization and bucketization. Recent work has shown that generalization loses considerable amount of information for high dimensional data, the bucketization does not prevent membership disclosure and does not make clear separation between quasi-identifying attributes and sensitive attributes. In this work a novel technique called Fragmentation is proposed for publishing sensitive data with preventing the sensitive information of the individual. Here first the vertical Fragmentation is applied to attributes. In vertical Fragmentation, attributes are segmented into columns. Each column contains a subset of attributes. Secondly, the horizontal Fragmentation is applied to tuples. In this, tuples are segmented into buckets. Each bucket contains a subset of tuples. Finally the real dataset is used for experiments and the results show that this Fragmentation technique preserves better utility while protecting privacy threats and prevents the membership disclosure.

Keywords


Privacy, Privacy Preservation, Data Anonymization, Data Publishing, Data Security.

References