Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource PHD PhD PhD Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Relation A related resource 61000306 Title A name given to the resource Development of Privacy Preserving Machine Learning Techniques Using Secure Multi-Party Computation Subject The topic of the resource Computer Science Description An account of the resource Machine learning (ML) has brought about a paradigm shift in insight generation across various domains, including healthcare, finance, and pharma, by leveraging historical data. However, the effectiveness of ML solutions hinges on the seamless collaboration between data owners, model owners, and ML clients while ensuring that privacy concerns are meticulously addressed. Unfortunately, existing privacy-preserving solutions have not been able to offer efficient and confidential ML training and inference. This has led to an increased focus on Privacy-Preserving Machine Learning (PPML), which has become a flourishing area of research aimed at safeguarding the privacy of machine learning stakeholders. In this regard, the present research introduces novel techniques for private ML inference and training of models using Secure Multi-Party Computation (SMPC) and Differential Privacy (DP) methods on horizontally and vertically partitioned datasets. The proposed techniques are implemented using Python with open-source libraries such as SyMPC and PyDP to ensure confidential inference and model protection. The findings across various parameters illustrate the effectiveness of the suggested techniques in addressing the privacy concerns of model owners and inference clients, with no significant impact on accuracy and a linear influence on performance as the privacy parameters, such as secure nodes count within the SMPC cluster. are increased. Furthermore, the privacy gain is substantiated by information privacy measures such as Mutual Information and KL-Divergence across different privacy budgets, which demonstrate empirically that privacy can be preserved with high ML accuracy and minimal performance cost. Creator An entity primarily responsible for making the resource Tiwari, Kapil Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Contributor An entity responsible for making contributions to the resource George, Jossy P Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/548041" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/548041</a>