TITLE:
An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data
AUTHORS:
Sagarkumar Patel, Rachna Patel, Ashok Akbari, Srinivasa Reddy Mukkala
KEYWORDS:
Privacy Preservation Models, Multi Institutional Data, Bio Technologies, Clinical Trial and Pharmaceutical Industry
JOURNAL NAME:
Journal of Information Security,
Vol.14 No.4,
October
10,
2023
ABSTRACT: The deep learning models hold considerable potential
for clinical applications, but there are many challenges to successfully training deep learning models.
Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building
large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing,
including patient privacy, data de-identification, regulation, intellectual
property, and data storage. These difficulties have lessened the impracticality
of central data storage. In this survey, we will look at 24 research
publications that concentrate on machine learning approaches linked to privacy
preservation techniques for multi-institutional data, highlighting the multiple
shortcomings of the existing methodologies. Researching different approaches
will be made simpler in this case based on a number of factors, such as
performance measures, year of publication and journals, achievements of the
strategies in numerical assessments, and other factors. A technique analysis
that considers the benefits and drawbacks of the strategies is additionally
provided. The article also looks at some potential areas for future research as
well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative
evaluation of the approaches offers a thorough justification for the
research’s purpose.