Preservation of privacy in data mining has emerged as an absolute prerequisite for
exchanging confidential information in terms of data analysis, validation, and publishing …

The use of big data in various fields has led to a rapid increase in a wide variety of data
resources, and various data analysis technologies such as standardized data mining and …

Protecting the privacy of healthcare information is an important part of encouraging data
custodians to give accurate records so that mining may proceed with confidence. The …

Privacy and security risks arising from the application of different data mining techniques to
large institutional data repositories have been solely investigated by a new research …

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts
of data. Private or confidential data may be sanitized or suppressed before it is shared or …

Various data mining techniques can be used to discover useful knowledge from large
collections of data. However, there is a risk of disclosing sensitive information when data is …

The process of frequent itemset mining (FIM) within large-scale databases plays a significant
part in many knowledge discovery tasks, where, however, potential privacy breaches are …

Association rule hiding is the process of transforming a transaction database into a sanitized
version to protect sensitive knowledge and patterns. The challenge is to minimize the side …

Privacy preserving data mining is a new research field that aims to protect the private
information and avoid the leakage of this information during the data mining process. One of …

Privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for
sensitive knowledge from disclosure. Many of the researchers in this area have recently …