# Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

arXiv: https://arxiv.org/abs/2202.07253 · HTML: https://mlsanity.com/paper/2202.07253/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control](https://mlsanity.com/paper/2008.08753/) (2020)
- [Secure Deep Graph Generation with Link Differential Privacy](https://mlsanity.com/paper/2005.00455/) (2020)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Secure Social Recommendation based on Secret Sharing](https://mlsanity.com/paper/2002.02088/) (2020)
- [PrivColl: Practical Privacy-Preserving Collaborative Machine Learning](https://mlsanity.com/paper/2007.06953/) (2020)

## Built on by (most cited first, 17 of 17)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference](https://mlsanity.com/paper/2209.11904/) (2022)
- [CipherPrune: Efficient and Scalable Private Transformer Inference](https://mlsanity.com/paper/2502.16782/) (2025)
- [PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2305.16163/) (2023)
- [Online Vertical Federated Learning for Cooperative Spectrum Sensing](https://mlsanity.com/paper/2312.11363/) (2023)
- [Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees](https://mlsanity.com/paper/2209.01539/) (2022)
- [Federated Learning on Non-iid Data via Local and Global Distillation](https://mlsanity.com/paper/2306.14443/) (2023)
- [Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity](https://mlsanity.com/paper/2505.07239/) (2025)
- [Tree-based Models for Vertical Federated Learning: A Survey](https://mlsanity.com/paper/2504.02285/) (2025)
- [Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation](https://mlsanity.com/paper/2509.26063/) (2025)
- [P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network](https://mlsanity.com/paper/2410.13905/) (2024)
- [Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2503.09184/) (2025)
- [Secure Embedding Aggregation for Federated Representation Learning](https://mlsanity.com/paper/2206.09097/) (2022)
- [Active-Passive Federated Learning for Vertically Partitioned Multi-view Data](https://mlsanity.com/paper/2409.04111/) (2024)
- [Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems](https://mlsanity.com/paper/2409.15558/) (2024)
- [Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2510.14894/) (2025)
- [GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2604.11659/) (2026)

## Abstract (arXiv)

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g.,rating) and user-user social data are usually generated by different platforms, and both of which contain sensitive information. Therefore, "How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature" remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate the effectiveness of S3Rec.