# Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation

arXiv: https://arxiv.org/abs/2310.18606 · HTML: https://mlsanity.com/paper/2310.18606/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 85 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Label-Only Membership Inference Attacks](https://mlsanity.com/paper/2007.14321/) (2020)
- [Auditing Differentially Private Machine Learning: How Private is Private SGD?](https://mlsanity.com/paper/2006.07709/) (2020)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets](https://mlsanity.com/paper/2204.00032/) (2022)
- [Knock Knock, Who's There? Membership Inference on Aggregate Location Data](https://mlsanity.com/paper/1708.06145/) (2017)
- [Selective Differential Privacy for Language Modeling](https://mlsanity.com/paper/2108.12944/) (2021)
- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [Generalised Differential Privacy for Text Document Processing](https://mlsanity.com/paper/1811.10256/) (2018)
- [Just Fine-tune Twice: Selective Differential Privacy for Large Language Models](https://mlsanity.com/paper/2204.07667/) (2022)
- [A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations](https://mlsanity.com/paper/2011.10187/) (2020)
- [MGeo: Multi-Modal Geographic Pre-Training Method](https://mlsanity.com/paper/2301.04283/) (2023)
- [NLPMM: a Next Location Predictor with Markov Modeling](https://mlsanity.com/paper/2003.07037/) (2020)
- [Optimal Geo-Indistinguishable Mechanisms for Location Privacy](https://mlsanity.com/paper/1402.5029/) (2014)
- [Measuring Membership Privacy on Aggregate Location Time-Series](https://mlsanity.com/paper/1902.07456/) (2019)
- [Out-of-Town Recommendation with Travel Intention Modeling](https://mlsanity.com/paper/2101.12555/) (2021)
- [A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data](https://mlsanity.com/paper/2308.07426/) (2023)
- [Robust Fingerprint of Location Trajectories Under Differential Privacy](https://mlsanity.com/paper/2204.04792/) (2022)

## Built on by (most cited first, 2 of 2)

- [Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion](https://mlsanity.com/paper/2507.13366/) (2025)
- [MRP-LLM: Multitask Reflective Large Language Models for Privacy-Preserving Next POI Recommendation](https://mlsanity.com/paper/2412.07796/) (2024)

## Abstract (arXiv)

As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience for users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges. Our attack suite is released at https://github.com/KunlinChoi/POIPrivacy.