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SLICING: A NEW APPROACH FOR PRIVACY PRESERVING DATA PUBLISHING

Platform : DOT NET

IEEE Projects Years : 2012 - 13

Slicing: A New Approach for Privacy Preserving Data Publishing

Abstract

 

 

 

Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ‘-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.

 

 

 

 

 

 

 

 

 

 

 

 

Existing System

 

  • Generalization  and bucketization techniques are used for designing privacy preserving microdata.

 

  • Generalization loses considerable amount of information, especially for high dimensional

data.

 

  • Bucketization does not prevent membership disclosure.

 

  • Both approaches, attributes are partitioned into three categories:

 

 1) some attributes are identifiers that can uniquely identify an individual, such as Name or Social Security Number

 2) some attributes are Quasi Identifiers (QI), which the adversary may already know (possibly from other publicly available databases) and which, when taken together, can potentially identify an individual, e.g., Birthdate, Sex, and Zipcode.

3) some attributes are Sensitive Attributes (SAs), which are unknown to the adversary and are considered sensitive, such as Disease and Salary.

 

  • Both generalization and bucketization, does the process below
  1. Removes identifiers from the data and then partitions tuples into buckets.
  2.  The two techniques differ in the next step.
    1. Generalization transforms the QI-values in each bucket into “less specific but semantically consistent” values so that tuples in the same bucket cannot be distinguished by their QI values.
    2. In bucketization, one separates the SAs from the QIs by randomly permuting the SA values in each bucket.

 

 

 

 

 

Disadvantages

 

  • When publishing microdata, there are three types of privacy disclosure threats.
    • The first type is membership disclosure.

 

  • The second type is identity disclosure, which occurs when an individual is linked to a particular record in the released table.

 

  • The third type is attribute disclosure, which occurs when new information about some individuals is revealed, i.e., the released data make it possible to infer the attributes of an individual more accurately than it would be possible

before the release.

  • There is a loss of data in high dimensional data.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Diagrammatic Representation

 

 

 

 

 

 

 

 

 

 


                                                                       MEMBERS

 

 

 
   

 

 

 

 


HACK                                                                             HACK                                           HACK

IDENTIFIER DATA                            MEMBERSHIP DATA                 SENSITIVE ATTRIBUTE

 

                                                            

INTRUDER                               INTRUDER                                    INTRUDER

 (IDENTITIY DISCLOSURE)        (MEMBERSHIP DISCLOSURE)                (ATTRIBUTE DISCLOSURE)

 

                                                                                                                               

 

Proposed system

 

  • A novel technique called slicing, which partitions the data both horizontally and vertically.
  • It preserves better data utility than generalization.
  • It  can be used for membership disclosure protection.
  • High Dimensional data can be handled.
  • Slicing  partition tuples into buckets.
  • Each bucket contains a subset of tuples. This horizontally partitions the table.
  • Within each bucket, values in each column are randomly permutated to break the linking between different columns.

 

  • Slicing provide protection against membership disclosure and attribute disclosure.

 

Advantages

 

  • l- Diverse slicing is used for attribute disclosure protection.

 

  • In the tuple partitioning phase, tuples are partitioned into buckets.

 

  • Slicing preserves better data utility than generalization.

 

  • Slicing is more effective than bucketization in workloads involving the sensitive attribute

 

  • The sliced table can be computed efficiently.



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