# Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2509.03024 · HTML: https://mlsanity.com/paper/2509.03024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption](https://mlsanity.com/paper/2104.03152/) (2021)
- [F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)](https://mlsanity.com/paper/2109.05371/) (2021)
- [Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2503.09184/) (2025)
- [HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption](https://mlsanity.com/paper/2506.18150/) (2025)

## Built on by (most cited first, 1 of 1)

- [GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2604.11659/) (2026)

## Abstract (arXiv)

In today's data-driven world, recommendation systems personalize user experiences across industries but rely on sensitive data, raising privacy concerns. Fully homomorphic encryption (FHE) can secure these systems, but a significant challenge in applying FHE to recommendation systems is efficiently handling the inherently large and sparse user-item rating matrices. FHE operations are computationally intensive, and naively processing various sparse matrices in recommendation systems would be prohibitively expensive. Additionally, the communication overhead between parties remains a critical concern in encrypted domains. We propose a novel approach combining Compressed Sparse Row (CSR) representation with FHE-based matrix factorization that efficiently handles matrix sparsity in the encrypted domain while minimizing communication costs. Our experimental results demonstrate high recommendation accuracy with encrypted data while achieving the lowest communication costs, effectively preserving user privacy.