Introduction to Secure Multi-Party Computation
Secure Multi-Party Computation (MPC) is a subfield of cryptography that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. This revolutionary concept allows organizations to collaborate on data analysis without revealing their sensitive information.
The Privacy Paradox
In our data-driven world, we face a fundamental paradox:
- Data utility: More data leads to better insights, predictions, and services
- Privacy requirements: Legal, ethical, and business requirements demand data protection
Secure computation technologies bridge this gap by enabling “privacy-preserving computation” - the ability to compute on encrypted data without decrypting it.
Key Concepts
What is Secure Multi-Party Computation?
MPC allows multiple parties to compute a joint function f(x₁, x₂, …, xₙ) where each party i holds a private input xᵢ. The computation reveals only the output of the function, not the individual inputs.
Security Properties
A secure MPC protocol guarantees:
- Privacy: No party learns anything beyond what can be inferred from the output
- Correctness: The computed result is accurate
- Input independence: Parties must choose inputs before learning others’ inputs
- Guaranteed output delivery: Honest parties receive the correct output
Real-World Applications
Financial Services
- Joint fraud detection across banks
- Private benchmarking and risk assessment
- Regulatory compliance without data sharing
Healthcare
- Medical research on combined datasets
- Drug discovery collaborations
- Epidemiological studies
Technology
- Private recommendation systems
- Collaborative filtering
- Privacy-preserving analytics
Technology Stack
This book covers several complementary technologies:
Secure Multi-Party Computation (MPC)
Cryptographic protocols for joint computation
Homomorphic Encryption
Encryption schemes that allow computation on ciphertext
Differential Privacy
Mathematical framework for quantifying and limiting privacy loss
Federated Learning
Distributed machine learning preserving data locality
About the Author
This book is written based on practical experience developing privacy-preserving systems at EAGLYS Inc., where I worked on the DataArmorGate DB project - a database proxy that enables SQL queries on encrypted data.
Md. Al-Amin Khandaker, Ph.D.
Research Engineer (Former) at EAGLYS Inc.
Cybersecurity Engineer at ITK Engineering GmbH