# Privacy-Preserving Synthetic Data Generation for Recommendation Systems

arXiv: https://arxiv.org/abs/2209.13133 · HTML: https://mlsanity.com/paper/2209.13133/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2022.

## Built on by (most cited first, 15 of 15)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation](https://mlsanity.com/paper/2301.11489/) (2023)
- [PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2305.16163/) (2023)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Synthetic Data: Revisiting the Privacy-Utility Trade-off](https://mlsanity.com/paper/2407.07926/) (2024)
- [Multi-queue Momentum Contrast for Microvideo-Product Retrieval](https://mlsanity.com/paper/2212.11471/) (2022)
- [Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?](https://mlsanity.com/paper/2307.07047/) (2023)
- [Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems](https://mlsanity.com/paper/2311.03488/) (2023)
- [Flexible Generation of Preference Data for Recommendation Analysis](https://mlsanity.com/paper/2407.16594/) (2024)
- [BehaveGPT: A Foundation Model for Large-scale User Behavior Modeling](https://mlsanity.com/paper/2505.17631/) (2025)
- [Privacy-Preserving Fair Synthetic Tabular Data](https://mlsanity.com/paper/2503.02968/) (2025)
- [Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains](https://mlsanity.com/paper/2408.08399/) (2024)
- [Large language model as user daily behavior data generator: balancing population diversity and individual personality](https://mlsanity.com/paper/2505.17615/) (2025)
- [RaMark: Radioactive Watermarking for Generated Tabular Data](https://mlsanity.com/paper/2607.09000/) (2026)

## Abstract (arXiv)

Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when collecting the users' behavior data for building the recommendation model. However, existing privacy-preserving solutions are designed for tackling the privacy issue only during the model training and results collection phases. The problem of privacy leakage still exists when directly sharing the private user interaction data with organizations or releasing them to the public. To address this problem, in this paper, we present a User Privacy Controllable Synthetic Data Generation model (short for UPC-SDG), which generates synthetic interaction data for users based on their privacy preferences. The generation model aims to provide certain privacy guarantees while maximizing the utility of the generated synthetic data at both data level and item level. Specifically, at the data level, we design a selection module that selects those items that contribute less to a user's preferences from the user's interaction data. At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences. Furthermore, we also present a privacy-utility trade-off strategy to balance the privacy and utility of the synthetic data. Extensive experiments and ablation studies have been conducted on three publicly accessible datasets to justify our method, demonstrating its effectiveness in generating synthetic data under users' privacy preferences.