Paper 2026/103

When Only Parts Matter: Efficient Privacy-Preserving Analytics with Fully Homomorphic Encryption

Abstract

The increasing reliance on cloud-based computation for data-intensive applications raises critical concerns about data confidentiality. Fully Homomorphic Encryption (FHE) provides strong theoretical guarantees by allowing computations over encrypted data, but its high computational cost limits its practicality in large-scale scenarios such as image analysis or matrix-based workloads. In this work, we introduce $\Pi_{ROI}$, a hybrid privacy-preserving computation protocol that leverages region-based selective encryption. The core idea is to encrypt only the sensitive Regions of Interest (ROIs) under an FHE scheme, while keeping the remaining, non-sensitive parts of the data in plaintext. This approach achieves end-to-end confidentiality for sensitive regions while significantly improving computational efficiency. We formally define the security of $\Pi_{ROI}$ through an ideal functionality $\mathcal{F}_{\text{proc}}$ and prove that it securely realizes $\mathcal{F}_{\text{proc}}$ against a semi-honest cloud service provider under standard cryptographic assumptions (IND-CPA, IND-CCA2, EUF-CMA, and collision-resistance). Experimental evaluation demonstrates that $\Pi_{ROI}$ offers substantial performance gains in mixed-sensitivity workloads.