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Sensitive Label Privacy Preservation with Anatomization for
Data Publishing
ABSTRACT:
Data in its original form, however, typically contain sensitive information about
individuals. Directly publishing raw data will violate the privacy of people involed.
Consequently, it becomes increasingly important to preserve the privacy of
published data. An attacker is apt to identify an individual from the published
tables, with attacks through the record linkage, attribute linkage, table linkage or
probabilistic attack. Although algorithms based on generalization and suppression
have been proposed to protect the sensitive attributes and resist these multiple
types of attacks, they often suffer from large information loss by replacing specific
values with more general ones. Alternatively, anatomization and permutation
operations can de-link the relation between attributes without modifying them. In
this paper, we propose a scheme Sensitive Label Privacy Preservation with
Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes
two procedures, table division and group division. During the table division, we
adopt entropy and mean-square contingency coefficient to partition attributes into
separate tables to inject uncertainty for reconstructing the original table. During the
group division, all the individuals in the original table are partitioned into non-
overlapping groups so that the published data satisfies the pre-defined privacy
requirements of our (α, β, γ, δ) model. Two comprehensive sets of real-world
relationship data are applied to evaluate the performance of our anonymization
approach. Simulations and privacy analysis show our scheme possesses better
privacy while ensuring higher utility.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium Dual Core.
 Hard Disk : 120 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 1 GB.
SOFTWARE REQUIREMENTS:
 Operating system : Windows 7.
 Coding Language : ASP.NET,C#.NET
 Tool : Visual Studio 2008
 Database : SQL SERVER 2005
REFERENCE:
Lin Yao ; Zhenyu Chen ; Xin Wang ; Dong Liu ; Guowei Wu, “Sensitive Label
Privacy Preservation with Anatomization for Data Publishing”, IEEE Transactions
on Dependable and Secure Computing, 2019.

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Sensitive Label Privacy Preservation with Anatomization for Data Publishing

  • 1. Sensitive Label Privacy Preservation with Anatomization for Data Publishing ABSTRACT: Data in its original form, however, typically contain sensitive information about individuals. Directly publishing raw data will violate the privacy of people involed. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Although algorithms based on generalization and suppression have been proposed to protect the sensitive attributes and resist these multiple types of attacks, they often suffer from large information loss by replacing specific values with more general ones. Alternatively, anatomization and permutation operations can de-link the relation between attributes without modifying them. In this paper, we propose a scheme Sensitive Label Privacy Preservation with Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes two procedures, table division and group division. During the table division, we adopt entropy and mean-square contingency coefficient to partition attributes into separate tables to inject uncertainty for reconstructing the original table. During the group division, all the individuals in the original table are partitioned into non- overlapping groups so that the published data satisfies the pre-defined privacy requirements of our (α, β, γ, δ) model. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization
  • 2. approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium Dual Core.  Hard Disk : 120 GB.  Monitor : 15’’ LED  Input Devices : Keyboard, Mouse  Ram : 1 GB. SOFTWARE REQUIREMENTS:  Operating system : Windows 7.  Coding Language : ASP.NET,C#.NET  Tool : Visual Studio 2008  Database : SQL SERVER 2005 REFERENCE: Lin Yao ; Zhenyu Chen ; Xin Wang ; Dong Liu ; Guowei Wu, “Sensitive Label Privacy Preservation with Anatomization for Data Publishing”, IEEE Transactions on Dependable and Secure Computing, 2019.