Privacy-Preserving Cloud Analytics Using Homomorphic Encryption

Abstract

Privacy-preserving cloud analytics represents a paradigm shift in data science: organizations can exploit the virtually unlimited computational resources of cloud platforms without exposing sensitive information. Homomorphic encryption (HE), which enables arithmetic and logical operations directly on encrypted data, is at the forefront of this transformation. This manuscript elaborates on a comprehensive HE-based framework tailored for cloud analytics, focusing on both theoretical foundations and practical implementation. We begin by detailing the mathematical underpinnings of HE schemes, with an emphasis on the Fan–Vercauteren (FV) algorithm and its leveled variants. Next, we describe the system architecture, encompassing client-side encryption, a cloud-based encrypted computation engine, and client-side decryption. Through a prototype built on Microsoft SEAL, we execute representative analytics tasks—summation, averaging, and single-variable linear regression—over datasets sized from 10 K to 100 K records. Our evaluation examines key performance metrics: latency per operation, throughput, ciphertext expansion, and result accuracy. A detailed statistical analysis table contrasts plaintext versus encrypted execution, highlighting the trade‑offs between privacy and performance. The manuscript concludes with a discussion of deployment considerations—such as key management, parameter selection for 128-bit security, and integration with existing big‑data tools—and outlines future research directions, including support for richer query languages, bootstrapping optimizations, and hybrid schemes combining HE with secure enclaves. This work provides a roadmap for practitioners seeking to deploy privacy‑preserving analytics in real‑world cloud environments, balancing strong confidentiality guarantees with acceptable performance and scalability.