A Hybrid Data Transformation Approach for Privacy Preserving Clustering of Categorical Data

In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also, some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and therefore have stability issues. Thus, handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 an MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data but cannot be used robustly for hybrid data. In this article, the authors generalize the MMeR algorithm with neighborhood relations and make it a neighborhood rough set model which this article calls MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data. Also, the authors have extended the MMeNR method to make it suitable for various applications like geospatial data analysis and epidemiology.

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Privacy-Preserving Process Mining in Healthcare

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17051612 ◽

2020 ◽

Vol 17 (5) ◽

pp. 1612 ◽

Cited By ~ 4

Author(s):

Anastasiia Pika ◽

Moe T. Wynn ◽

Stephanus Budiono ◽

Arthur H.M. ter Hofstede ◽

Wil M.P. van der Aalst ◽

...

Keyword(s):

Data Mining ◽

Data Privacy ◽

Process Mining ◽

Personal Data ◽

Data Transformation ◽

Privacy Preserving ◽

Sensitive Information ◽

Process Data ◽

Mining Community ◽

Healthcare Process

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

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