Abstract

Privacy‑preserving data analytics has become a foundational requirement in scenarios where sensitive datasets must be analyzed without exposing underlying information. Homomorphic Encryption (HE) is a class of cryptographic techniques that enables computation directly on encrypted data, such that results remain encrypted until the data owner decrypts them. This property allows untrusted processors (e.g., third‑party cloud servers) to perform analytics without ever accessing plaintext data. Fully Homomorphic Encryption (FHE) extends this capability to arbitrary computations on encrypted data, a breakthrough first theoretically introduced in the late 20th century and progressively made practical by subsequent research. Libraries such as Microsoft SEAL, HElib, HEAAN, and OpenFHE provide practical toolkits for implementing HE schemes in real systems, supporting additions, multiplications, and complex arithmetic on ciphertexts with provable security guarantees.Wikipedia+3Wikipedia+3Wikipedia+3

HE evaluation typically balances privacy guarantees, computational overhead, and accuracy of analytics results. Prior work demonstrates that HE can support a range of privacy‑preserving analytics tasks, from basic statistical operations to machine learning and federated learning. Specific applications include smart meter data analytics, where lattice‑based HE enables confidential energy usage analysis, and rare disease genomic studies, where HE enables multi‑institution collaboration without sharing protected health information.OUP Academic+1

Despite its strengths, HE schemes incur significant computational cost and demand careful engineering to maintain performance. Frameworks that combine HE with other privacy techniques (e.g., differential privacy or zero‑knowledge proofs) can mitigate performance and security trade‑offs. Furthermore, real‑world systems often integrate HE into larger privacy preserving analytics pipelines (e.g., federated analytics), demonstrating that HE is a key enabler for confidential and collaborative analytics across domains.Informatica+1

This paper investigates existing frameworks that utilize HE for privacy‑preserving analytics, evaluates how they balance privacy and utility, and proposes design principles for future scalable HE systems. Findings show that while HE introduces overhead, it offers strong privacy without requiring data decryption, making it impactful for sensitive analytics tasks in healthcare, finance, and smart infrastructure.