GuardML: Efficient Privacy-Preserving Machine Learning Services Through
  Hybrid Homomorphic Encryption
Eugene Frimpong Khoa Nguyen Mindaugas Budzys Tanveer Khan Antonis Michalas
Machine Learning (ML) has emerged as one of data science's most
transformative and influential domains. However, the widespread adoption of ML
introduces privacy-related concerns owing to the increasing number of malicious
attacks targeting ML models. To address these concerns, Privacy-Preserving
Machine Learning (PPML) methods have been introduced to safeguard the privacy
and security of ML models. One such approach is the use of Homomorphic
Encryption (HE). However, the significant drawbacks and inefficiencies of
traditional HE render it impractical for highly scalable scenarios.
Fortunately, a modern cryptographic scheme, Hybrid Homomorphic Encryption
(HHE), has recently emerged, combining the strengths of symmetric cryptography
and HE to surmount these challenges. Our work seeks to introduce HHE to ML by
designing a PPML scheme tailored for end devices. We leverage HHE as the
fundamental building block to enable secure learning of classification outcomes
over encrypted data, all while preserving the privacy of the input data and ML
model. We demonstrate the real-world applicability of our construction by
developing and evaluating an HHE-based PPML application for classifying heart
disease based on sensitive ECG data. Notably, our evaluations revealed a slight
reduction in accuracy compared to inference on plaintext data. Additionally,
both the analyst and end devices experience minimal communication and
computation costs, underscoring the practical viability of our approach. The
successful integration of HHE into PPML provides a glimpse into a more secure
and privacy-conscious future for machine learning on relatively constrained end
devices.