Privacy Preserving Data Mining Operations without Disrupting Data Quality

Persistent Link:
http://hdl.handle.net/10150/271473
Title:
Privacy Preserving Data Mining Operations without Disrupting Data Quality
Author:
B.Swapna; R.VijayaPrakash
Affiliation:
Department of CSE, SR Engineering College Warangal, Andhra Pradesh, India; Associate Professor in Department of CSE SR Engineering College Warangal, Andhra Pradesh, India
Publisher:
International Journal of Computer Science and Network (IJCSN)
Journal:
International Journal of Computer Science and Network (IJCSN)
Issue Date:
1-Dec-2012
Description:
Data mining operations have become prevalent as they can extract trends or patterns that help in taking good business decisions. Often they operate on large historical databases or data warehouses to obtain actionable knowledge or business intelligence that helps in taking well informed decisions. In the data mining domain there came many tools to perform data mining operations. These tools are best used to obtain actionable knowledge from data. Manually doing this is not possible as the data is very huge and takes lot of time. Thus the data mining domain is being improved in a rapid pace. While data mining operations are very useful in obtaining business intelligence, they also have some drawbacks that are they get sensitive information from the database. People may misuse the freedom given by obtaining sensitive information illegally. Preserving privacy of data is also important. Towards this end many Privacy Preserving Data Mining (PPDM) algorithms came into existence that sanitize data to prevent data mining algorithms from extracting sensitive information from the databases.
URI:
http://hdl.handle.net/10150/271473
Additional Links:
http://ijcsn.org/IJCSN-2012/1-6/IJCSN-2012-1-6-17.pdf
Abstract:
Data mining operations help discover business intelligence from historical data. The extracted business intelligence or actionable knowledge helps in taking well informed decisions that leads to profit to the organization that makes use of it. While performing mining privacy of data has to be given utmost importance. To achieve this PPDM (Privacy Preserving Data Mining) came into existence by sanitizing database that prevents discovery of association rules. However, this leads to modification of data and thus disrupting the quality of data. This paper proposes a new technique and algorithms that can perform privacy preserving data mining operations while ensuring that the data quality is not lost. The empirical results revealed that the proposed technique is useful and can be used in real world applications.
Type:
Technical Report
Language:
en
Keywords:
data mining; PPDM; sanitization; algorithms
Series/Report no.:
IJCSN-2012-1-6-17; 6
ISSN:
2277-5420

Full metadata record

DC FieldValue Language
dc.contributor.authorB.Swapnaen_US
dc.contributor.authorR.VijayaPrakashen_US
dc.date.accessioned2013-03-07T23:33:32Z-
dc.date.available2013-03-07T23:33:32Z-
dc.date.issued2012-12-01-
dc.identifier.issn2277-5420-
dc.identifier.urihttp://hdl.handle.net/10150/271473-
dc.descriptionData mining operations have become prevalent as they can extract trends or patterns that help in taking good business decisions. Often they operate on large historical databases or data warehouses to obtain actionable knowledge or business intelligence that helps in taking well informed decisions. In the data mining domain there came many tools to perform data mining operations. These tools are best used to obtain actionable knowledge from data. Manually doing this is not possible as the data is very huge and takes lot of time. Thus the data mining domain is being improved in a rapid pace. While data mining operations are very useful in obtaining business intelligence, they also have some drawbacks that are they get sensitive information from the database. People may misuse the freedom given by obtaining sensitive information illegally. Preserving privacy of data is also important. Towards this end many Privacy Preserving Data Mining (PPDM) algorithms came into existence that sanitize data to prevent data mining algorithms from extracting sensitive information from the databases.en_US
dc.description.abstractData mining operations help discover business intelligence from historical data. The extracted business intelligence or actionable knowledge helps in taking well informed decisions that leads to profit to the organization that makes use of it. While performing mining privacy of data has to be given utmost importance. To achieve this PPDM (Privacy Preserving Data Mining) came into existence by sanitizing database that prevents discovery of association rules. However, this leads to modification of data and thus disrupting the quality of data. This paper proposes a new technique and algorithms that can perform privacy preserving data mining operations while ensuring that the data quality is not lost. The empirical results revealed that the proposed technique is useful and can be used in real world applications.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Science and Network (IJCSN)en_US
dc.relation.ispartofseriesIJCSN-2012-1-6-17en_US
dc.relation.ispartofseries6en_US
dc.relation.urlhttp://ijcsn.org/IJCSN-2012/1-6/IJCSN-2012-1-6-17.pdfen_US
dc.subjectdata miningen_US
dc.subjectPPDMen_US
dc.subjectsanitizationen_US
dc.subjectalgorithmsen_US
dc.titlePrivacy Preserving Data Mining Operations without Disrupting Data Qualityen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentDepartment of CSE, SR Engineering College Warangal, Andhra Pradesh, Indiaen_US
dc.contributor.departmentAssociate Professor in Department of CSE SR Engineering College Warangal, Andhra Pradesh, Indiaen_US
dc.identifier.journalInternational Journal of Computer Science and Network (IJCSN)en_US
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