# Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation

arXiv: https://arxiv.org/abs/2202.04893 · HTML: https://mlsanity.com/paper/2202.04893/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [DDTCDR: Deep Dual Transfer Cross Domain Recommendation](https://mlsanity.com/paper/1910.05189/) (2019)
- [CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network](https://mlsanity.com/paper/2005.10549/) (2020)
- [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019)
- [Fast Differentially Private Matrix Factorization](https://mlsanity.com/paper/1505.01419/) (2015)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction](https://mlsanity.com/paper/2106.02768/) (2021)
- [Secure Social Recommendation based on Secret Sharing](https://mlsanity.com/paper/2002.02088/) (2020)
- [Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization](https://mlsanity.com/paper/2003.05610/) (2020)
- [Wishart Mechanism for Differentially Private Principal Components Analysis](https://mlsanity.com/paper/1511.05680/) (2015)
- [Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation](https://mlsanity.com/paper/2009.06884/) (2020)

## Built on by (most cited first, 22 of 22)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation](https://mlsanity.com/paper/2310.11730/) (2023)
- [Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems](https://mlsanity.com/paper/2310.05847/) (2023)
- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security](https://mlsanity.com/paper/2410.02191/) (2024)
- [DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2209.10163/) (2022)
- [PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2305.16163/) (2023)
- [Federated Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2410.08249/) (2024)
- [A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation](https://mlsanity.com/paper/2403.03600/) (2024)
- [Federated Learning on Non-iid Data via Local and Global Distillation](https://mlsanity.com/paper/2306.14443/) (2023)
- [Protecting Split Learning by Potential Energy Loss](https://mlsanity.com/paper/2210.09617/) (2022)
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2308.08072/) (2023)
- [Defending Label Inference Attacks in Split Learning under Regression Setting](https://mlsanity.com/paper/2308.09448/) (2023)
- [FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling](https://mlsanity.com/paper/2403.02630/) (2024)
- [HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation](https://mlsanity.com/paper/2205.12042/) (2022)
- [In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems](https://mlsanity.com/paper/2309.01335/) (2023)
- [BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation](https://mlsanity.com/paper/2412.02934/) (2024)
- [Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces](https://mlsanity.com/paper/2311.01722/) (2023)
- [Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems](https://mlsanity.com/paper/2409.15558/) (2024)
- [DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation](https://mlsanity.com/paper/2410.10835/) (2024)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systems](https://mlsanity.com/paper/2603.12752/) (2026)

## Abstract (arXiv)

Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve the recommendation performance of a target domain by leveraging the data of other source domains. However, most existing CDR models assume information can directly 'transfer across the bridge', ignoring the privacy issues. To solve the privacy concern in CDR, in this paper, we propose a novel two stage based privacy-preserving CDR framework (PriCDR). In the first stage, we propose two methods, i.e., Johnson-Lindenstrauss Transform (JLT) based and Sparse-awareJLT (SJLT) based, to publish the rating matrix of the source domain using differential privacy. We theoretically analyze the privacy and utility of our proposed differential privacy based rating publishing methods. In the second stage, we propose a novel heterogeneous CDR model (HeteroCDR), which uses deep auto-encoder and deep neural network to model the published source rating matrix and target rating matrix respectively. To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain. We conduct experiments on two benchmark datasets and the results demonstrate the effectiveness of our proposed PriCDR and HeteroCDR.