# Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach

arXiv: https://arxiv.org/abs/2309.11515 · HTML: https://mlsanity.com/paper/2309.11515/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 76 references that are in the map.
- First posted on arXiv 17 Sep 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Stealing Links from Graph Neural Networks](https://mlsanity.com/paper/2005.02131/) (2020)
- [Sequential Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2106.14226/) (2021)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis](https://mlsanity.com/paper/2108.06504/) (2021)
- [GraphMI: Extracting Private Graph Data from Graph Neural Networks](https://mlsanity.com/paper/2106.02820/) (2021)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [Memory Augmented Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/1912.11730/) (2019)
- [GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation](https://mlsanity.com/paper/2203.00949/) (2022)
- [Graph Embedding for Recommendation against Attribute Inference Attacks](https://mlsanity.com/paper/2101.12549/) (2021)
- [Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective](https://mlsanity.com/paper/2107.01475/) (2021)
- [Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning](https://mlsanity.com/paper/1911.09872/) (2019)
- [Fast Differentially Private Matrix Factorization](https://mlsanity.com/paper/1505.01419/) (2015)
- [Linear Program Reconstruction in Practice](https://mlsanity.com/paper/1810.05692/) (2018)
- [NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/2106.11865/) (2021)
- [LPGNet: Link Private Graph Networks for Node Classification](https://mlsanity.com/paper/2205.03105/) (2022)
- [Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy](https://mlsanity.com/paper/2202.10209/) (2022)
- [Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://mlsanity.com/paper/2205.10759/) (2022)

## Built on by (most cited first, 3 of 3)

- [Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses](https://mlsanity.com/paper/2508.19641/) (2025)
- [Efficient and Privacy-Preserved Link Prediction via Condensed Graphs](https://mlsanity.com/paper/2503.12156/) (2025)
- [An Analytical Approach to Privacy and Performance Trade-Offs in Healthcare Data Sharing](https://mlsanity.com/paper/2508.18513/) (2025)

## Abstract (arXiv)

With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted to preserve privacy in recommender systems. However, existing differentially private recommender systems only consider static and independent interactions, so they cannot apply to sequential recommendation where behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.