CKKS for Approximate Arithmetic & ML
Apply CKKS to privacy-preserving machine learning inference.
Why CKKS?
BGV/BFV work over integers (exact arithmetic). Machine learning uses floating-point numbers (approximate arithmetic). CKKS (Cheon-Kim-Kim-Song, 2017) encodes real/complex numbers and allows controlled precision loss — ideal for ML inference and data analytics.
Approximate Arithmetic
CKKS treats the noise as part of the encoding precision. Instead of eliminating noise (error correcting), CKKS views the result as an approximation with guaranteed precision bounds. This trade-off enables much more efficient HE for continuous-valued computations.
All lessons in this course
- What Is Homomorphic Encryption?
- Learning With Errors (LWE) Foundation
- BGV & BFV Schemes for Integer Operations
- CKKS for Approximate Arithmetic & ML