Slicing: A New Approach to Privacy Preserving Data Publishing

Slicing: A New Approach to Privacy Preserving Data Publishing

Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that general ization 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 â„“-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.

Slicing: A New Approach to Privacy Preserving Data Publishing

Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that general ization 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 â„“-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.

7 thoughts on “Slicing: A New Approach to Privacy Preserving Data Publishing

  • February 15, 2013 at 3:54 pm
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    i need the project

    Reply
  • February 17, 2013 at 5:06 am
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    i want full project in Slicing: A New Approach for Privacy Preserving Data Publishing

    thank you sir

    Reply
  • March 31, 2013 at 8:17 am
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    Please any one help me, I require

    “Slicing: A New Approach to Privacy Preserving Data Publishing – projects 2012”

    project argently in j2ee and documentation if possible.

    Reply
    • February 25, 2015 at 9:31 am
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      Can u plz send overall project with documentation as soon as possible Thanks in advance 🙂

      Reply
    • February 25, 2015 at 9:31 am
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      Can u plz send overall project with documentation as soon as possible Thanks in advance 🙂

      Reply
  • June 6, 2013 at 2:20 pm
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    i need the full project sir. the whole project with documentation. can anyone help me please…

    Reply
  • March 25, 2015 at 6:37 am
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    sir/mam iwant “slicing a new approach to privacy preserving data publishing” total project.can u send me pls

    Reply

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