An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

Abstract

The deep learning models hold considerable potential for clinical applicatio ns, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possib le 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.