Privacy-preserving trajectory classification of driving trip data based on pattern discovery techniques

2017 ◽  
Author(s):  
Gene P.K. Wu ◽  
Keith C.C. Chan
Keyword(s):  
Pattern Discovery ◽  
Privacy Preserving ◽  
Trajectory Classification

Bucket trajectory classification of mining excavators

2013 ◽  
Vol 31 ◽  
pp. 128-139 ◽  
Author(s):  
Rashi Tiwari ◽  
Jeremy Knowles ◽  
George Danko
Keyword(s):  
Trajectory Classification

Functional classification of proteins by pattern discovery and top-down clustering of primary sequences

2001 ◽  
Vol 40 (2) ◽  
pp. 379-393 ◽  
Author(s):  
A. H. Liu ◽  
A. Califano
Keyword(s):  
Pattern Discovery ◽  
Functional Classification ◽  
Top Down

scholarly journals SW-SDF based privacy preserving data classification

2013 ◽  
Vol 4 (3) ◽  
pp. 813-820
Author(s):  
Kiran P ◽  
Kavya N. P.
Keyword(s):  
Privacy Preservation ◽  
Data Classification ◽  
Privacy Preserving ◽  
Information Loss ◽  
Personal Privacy ◽  
Privacy Preserving Data Mining ◽  
The Core ◽  
Sensitive Attribute ◽  
Mining Algorithms

The core objective of privacy preserving data mining is to preserve the confidentiality of individual even after mining. The basic advantage of personalized privacy preservation is that the information loss is very less as compared with other privacy preservation algorithms. These algorithms how ever have not been designed for specific mining algorithms. SW-SDF personalized privacy preservation uses two flags SW and SDF. SW is used for assigning a weight for the sensitive attribute and SDF for sensitive disclosure which is accepted from individual. In this paper we have designed an algorithm which uses SW-SDF personal privacy preservation for data classification. This method ensures privacy and classification of data.

Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

2021 ◽  
pp. 569-588
Author(s):  
Boudheb Tarik ◽  
Elberrichi Zakaria
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Third Party ◽  
Distributed Data ◽  
Biomedical Data ◽  
Collaborative Models ◽  
Highly Sensitive ◽  
Complete Access

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.

Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

2018 ◽  
Vol 8 (3) ◽  
pp. 41-58
Author(s):  
Boudheb Tarik ◽  
Elberrichi Zakaria
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Third Party ◽  
Distributed Data ◽  
Biomedical Data ◽  
Collaborative Models ◽  
Highly Sensitive ◽  
Complete Access

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.

Privacy-Preserving Classification of Customer Data without Loss of Accuracy

2005 ◽  
Author(s):  
Zhiqiang Yang ◽  
Sheng Zhong ◽  
Rebecca N. Wright
Keyword(s):  
Privacy Preserving ◽  
Customer Data
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