The objective of this project is to develop a privacy-preserving data analytics system using secure multi-party computation. The system enables multiple parties to collaboratively compute results without revealing their private datasets to each other.
Study privacy risks in collaborative data analytics.
Understand secure multi-party computation (SMPC) principles.
Design a distributed analytics framework for multiple participants.
Implement secure computation protocols for shared analytics tasks.
Ensure that raw data never leaves participant environments.
Aggregate computation results securely.
Test system correctness using simulated datasets.
Measure computation overhead and performance impact.
Analyze security guarantees against data leakage.
Compare SMPC with traditional centralized analytics.
Document system benefits and challenges.
