Introduction
This document discusses the principles of encrypted data analysis. It will provide a high-level overview, aiming to provide an understanding without revealing the original data.
Encryption Overview
- What is encryption? Encryption is the process of encoding data in such a way that only authorized parties can access it.
- Why use encryption? Encryption protects sensitive data from unauthorized access.
- Types of encryption:
- Symmetric encryption uses the same key for encryption and decryption (e.g., AES).
- Asymmetric encryption uses a pair of keys (public and private) for encryption and decryption (e.g., RSA).
Encrypted Data Analysis
The Challenge
Analyzing data while maintaining confidentiality is a complex problem across many fields. Direct analysis is impossible because of the encryption, making it a challenge.
Techniques
- Homomorphic Encryption: Homomorphic encryption allows computations to be performed on encrypted data without decrypting it first. The results are also encrypted.
- Secure Multi-Party Computation (SMPC): SMPC enables multiple parties to perform computations on their combined datasets without revealing the individual datasets to each other.
- Differential Privacy: This provides a level of ‘noise’ or randomization to the data, providing protection while maintaining utility.
- Tokenization: Data values are replaced with non-sensitive equivalents. This maintains data utility while potentially reducing the risk of breach.
Conclusion
Encrypted data analysis has become increasingly central, offering a crucial layer of defense for sensitive data.
