Introduction

Sensitive features (client characteristics, portfolio composition, proprietary models) must remain encrypted even during computation. Homomorphic encryption enables ML inference on encrypted data: predictions computed without decrypting data, preserving privacy. Recent efficiency improvements make homomorphic encryption practical for some financial applications.

Homomorphic Encryption Basics

Encrypt data on client side. Send encrypted features to server. Server computes model inference directly on encrypted features. Return encrypted prediction. Client decrypts result. Server never sees unencrypted data. Enables secure outsourcing of computation.

Practical Considerations

Homomorphic encryption is 100-1000x slower than standard computation. Suitable for moderate-latency applications, not real-time trading. Significant overhead: encryption/decryption adds latency. Limited model types supported (deep learning harder than simple linear models).

Applications

Client-side risk assessment: client encrypts portfolio, sends to service for risk computation without revealing holdings. Privacy-preserving collaborative modeling: multiple parties jointly train models without sharing raw data. Compliance: ensures data never visible to service operator.

Conclusion

Homomorphic encryption enables privacy-preserving ML computation, valuable for sensitive financial applications despite computational overhead.