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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Sneha, M.; Augustine, Peter; Beaulah Soundarabai, P. Title A name given to the resource Leveraging Java for Developing Privacy-Preserving and Cross-Platform Machine Learning Applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 7th International Conference on Computing, Communication and Automation, ICCCA 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCCA66364.2025.11325318" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCA66364.2025.11325318</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035186652?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035186652?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sneha M., Christ University, Dept. Computer Science, Karanataka, Bengaluru, India; Augustine P., Christ University, Dept. Computer Science, Karanataka, Bengaluru, India; Beaulah Soundarabai P., Christ University, Dept. Computer Science, Karanataka, Bengaluru, India Description An account of the resource The growing focus on data privacy and system interoperability has created a clear need for machine learning (ML) applications. These applications must be able to protect sensitive data while maintaining consistent performance in various computing environments. Java is considered as a strong choice due to its platform independence, strong static typing, and rich ecosystem of development tools and libraries. This study checks how Java supports privacy-preserving techniques such as federated learning, differential privacy, and homomorphic encryption, instilling confidence in its role for secure ML development. It also reviews Java-based libraries, including Weka, Deeplearning4j, and Apache Spark, highlighting their role in building safe, scalable, and portable ML solutions. Through architectural analysis, benchmark-based evaluation, and comparisons with other programming languages, the study demonstrates the strengths of Java to deliver secure, scalable, and interoperable privacy-sensitive machine learning applications. 2025 IEEE. Subject The topic of the resource Cross-Platform Development; Differential Privacy; Federated Learning; Homomorphic Encryption; Java; Machine Learning; Privacy Preservation Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156980-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online