A Privacy-Preserving Framework for Collaborative Data Analysis Using Homomorphic Encryption
Abstract
Data sharing for collaborative analysis is often hindered by privacy concerns, particularly in healthcare and finance. This paper introduces a privacy-preserving framework leveraging homomorphic encryption to enable secure collaborative data analysis. The framework allows computations on encrypted data, ensuring privacy without compromising analytical capabilities. Case studies on healthcare diagnostics and financial risk assessment illustrate the framework’s effectiveness in maintaining data confidentiality while delivering accurate analytical outcomes.
References
Kotler, P., & Keller, K. L. (2012). Marketing management (14th ed.). Pearson Education.
Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy (8th ed.). Pearson.
Maxwell, J. A. (2013). Qualitative research design: An interactive approach (3rd ed.). Sage.
Mintzberg, H. (1994). The rise and fall of strategic planning. Free Press.
Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. Free Press.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Artificial Intelligence-Driven Predictive Analytics for Educational Behavior Assessment. Transactions on Latest Trends in Artificial Intelligence, 1(1). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/638
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Machine Learning Algorithms for Fraud Detection in Financial Transactions. International Journal of Sustainable Development in Computing Science, 2(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/639
Adusumilli, S., Damancharla, H., & Metta, A. (2021). Deep Learning Techniques for Image Recognition in Autonomous Vehicles. (2021). International Meridian Journal, 3(3). https://meridianjournal.in/index.php/IMJ/article/view/94
Adusumilli, S., Damancharla, H., & Metta, A. (2021). Integrating Machine Learning and Blockchain for Decentralized Identity Management Systems. (2021). International Journal of Machine Learning and Artificial Intelligence, 2(2). https://jmlai.in/index.php/ijmlai/article/view/46
Adusumilli, S., Damancharla, H., & Metta, A. (2022). Blockchain-Based Secure Framework for IoT Data Management. International Journal of Sustainable Development in Computing Science, 4(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/640
Adusumilli, S., Damancharla, H., & Metta, A. (2022). Optimizing Supply Chain Efficiency Through Blockchain and Smart Contracts. (2022). International Numeric Journal of Machine Learning and Robots, 6(6). https://injmr.com/index.php/fewfewf/article/view/183
Adusumilli, S. B. K., Damancharla, H., & Metta, A. R. (2021). AI-Powered Cybersecurity Solutions for Threat Detection and Prevention. International Journal of Creative Research In Computer Technology and Design, 3(3).
Adusumilli, S. B. K., Damancharla, H., & Metta, A. R. (2020). Leveraging AI for Real-Time Sentiment Analysis in Social Media Networks. International Numeric Journal of Machine Learning and Robots, 4(4).
Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021). Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model.
Adusumilli, S. B. K. Mitigating Cybersecurity Risks in Embedded Systems A Software-First Approach.
Whig, P., & Adusumilli, S. B. K. (2022). Machine Learning Applications in Healthcare Supply Chains: Improving Efficiency, Resilience, and Patient Outcomes. Transactions on Recent Developments in Health Sectors, 5(5).
