360 Privacy for Machine Learning

Fully Homomorphic Encryption (FHE) offers a dual advantage in machine learning by securing both user data and model parameters, addressing privacy concerns and intellectual property protection. FHE enables computations on encrypted data without revealing user information or model details, using schemes that support operations like additions and multiplications, and approximating non-linear functions. While encrypting models can be costly and slow, hosting models on company-trusted servers ensures protection without compromising user privacy, since data remains encrypted except on the user's device. An example on Hugging Face demonstrates a sentiment analysis model using FHE, highlighting its ability to protect user privacy and company interests simultaneously by performing encrypted computations on trusted servers. This approach, while maintaining user privacy, also counters black-box attacks by controlling query amounts, blending security with practical execution speed.