SECURE MULTI-PARTY COMPUTATION FOR PRIVACY PRESERVATION IN BIG DATA ANALYTICS

• Dr. Sarah Khan

  Institute of Data Science and Engineering, University of Punjab, Lahore, Pakistan.

With the increasing reliance on Big Data for decision-making across sectors, privacy concerns have become a significant challenge. Secure Multi-Party Computation (SMPC) offers a robust solution for privacy-preserving data analysis by allowing multiple parties to collaboratively compute a function over their combined data without exposing individual datasets. This article explores the potential of SMPC in ensuring data privacy while enabling insightful analytics in various domains, such as healthcare, finance, and social networks. By reviewing the latest advancements in SMPC protocols and their applications in Big Data, this paper provides a comprehensive understanding of how SMPC can enhance the security of data while maintaining its usability for analytics. The study also evaluates current challenges and future research directions in integrating SMPC into real-world Big Data systems.

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