CRYPPAR: An efficient framework for privacy preserving association rule mining over vertically partitioned data

Tran, Duc; Ng, Wee-Keong; Zha, Wei

doi:10.1109/tencon.2009.5395988

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…The cryptographic approaches are more popular because of the two reasons; it offers well defined model for the privacy; and a vast toolset of the cryptographic algorithms are available to construct the privacy preserving data mining. Tran, Ng, and Zha (2009, January), have developed cryptographic frame work for the privacy preserving association rule mining. CRYPPAR was the developed protocol which efficiently mines the association rule over the vertically partitioned data.…”

Section: Cryptographic Approachesmentioning

confidence: 99%

Survey on Privacy Preserving Association Rule Data Mining

Navale

Mali²

2017

International Journal of Rough Sets and Data Analysis

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The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

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Section: Cryptographic Approachesmentioning

confidence: 99%

Survey on Privacy Preserving Association Rule Data Mining

Navale

Mali²

2017

International Journal of Rough Sets and Data Analysis

View full text Add to dashboard Cite

show abstract

“…The privacy preserving approaches [5–12] secure the details that are confidential during the extraction of data from the database [13]. The authentication‐ and security‐based approaches [14–16] are proposed for providing the secure transmission of data. The objective of PPDM is to secure the data or provide privacy by seeking permission for the analysis of data [17].…”

Section: Introductionmentioning

confidence: 99%