23-02-2013, 10:02 AM
Slicing: A new Approach for Privacy Preserving Data Publishing
[attachment=51533]
ABSTRACT:
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data 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.
we present a novel technique called slicing, which partitions the data both horizontally and vertically.
INTRODUCTION:
Micro data contains records each of which contains information about an
individual entity, such as a person, a household, or an organization.
Several micro data anonymization techniques have been proposed.
The most popular ones are generalization for k-anonymity and bucketization for ‘l-diversity.
In both approaches, attributes are partitioned into three categories:
Identifiers
Quasi Identifiers (QI)
Sensitive Attributes (SAs)
Existing System:
Many existing clustering algorithms (e.g., k- means) requires the calculation of the “centroids”(The center of mass of a geometric object of uniform density).
Second, k- medoids (k -means algorithm and the medoid shift algorithm) method is very robust to the existence of outliers (i.e., data points that are very far away from the rest of data points).
The large number of data users and documents in preserving data, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need.
The searchable encryption focuses on single keyword search or Boolean keyword search, and rarely differentiates the search results.
Proposed System:
We present a novel technique called slicing, which partitions the data both horizontally and vertically.
Three types of encoding schemes have been proposed for generalization:
global recoding,
regional recoding,
local recoding.
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.
Original Data:
Our results confirm that slicing preserves much better data utility than generalization.
In workloads involving the sensitive attribute, slicing is also more effective than bucketization.
In some classification experiments, slicing shows better performance than using the original data.
Generalized Data:
Generalized Data, in order to perform data analysis or data mining tasks on the generalized table, the data analyst has to make the uniform distribution assumption that every value in a generalized interval/set is equally possible, as no other distribution assumption can be justified.
This significantly reduces the data utility of the generalized data.
Sliced Data:
Another important advantage of slicing is its ability to handle high-dimensional data.
By partitioning attributes into columns, slicing reduces the dimensionality of the data.
Each column of the table can be viewed as a sub-table with a lower dimensionality.
[attachment=51533]
ABSTRACT:
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data 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.
we present a novel technique called slicing, which partitions the data both horizontally and vertically.
INTRODUCTION:
Micro data contains records each of which contains information about an
individual entity, such as a person, a household, or an organization.
Several micro data anonymization techniques have been proposed.
The most popular ones are generalization for k-anonymity and bucketization for ‘l-diversity.
In both approaches, attributes are partitioned into three categories:
Identifiers
Quasi Identifiers (QI)
Sensitive Attributes (SAs)
Existing System:
Many existing clustering algorithms (e.g., k- means) requires the calculation of the “centroids”(The center of mass of a geometric object of uniform density).
Second, k- medoids (k -means algorithm and the medoid shift algorithm) method is very robust to the existence of outliers (i.e., data points that are very far away from the rest of data points).
The large number of data users and documents in preserving data, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need.
The searchable encryption focuses on single keyword search or Boolean keyword search, and rarely differentiates the search results.
Proposed System:
We present a novel technique called slicing, which partitions the data both horizontally and vertically.
Three types of encoding schemes have been proposed for generalization:
global recoding,
regional recoding,
local recoding.
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.
Original Data:
Our results confirm that slicing preserves much better data utility than generalization.
In workloads involving the sensitive attribute, slicing is also more effective than bucketization.
In some classification experiments, slicing shows better performance than using the original data.
Generalized Data:
Generalized Data, in order to perform data analysis or data mining tasks on the generalized table, the data analyst has to make the uniform distribution assumption that every value in a generalized interval/set is equally possible, as no other distribution assumption can be justified.
This significantly reduces the data utility of the generalized data.
Sliced Data:
Another important advantage of slicing is its ability to handle high-dimensional data.
By partitioning attributes into columns, slicing reduces the dimensionality of the data.
Each column of the table can be viewed as a sub-table with a lower dimensionality.