TrjPrivacy

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TKDE'23: A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

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# TrjPrivacy
Source code of TKDE'23 paper: [A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing](https://ieeexplore.ieee.org/document/9772978).

## Compared Models
- Formal models
  - k-anonymity models: W4M [1], GLOVE [2], KLT [3]
  - differential privacy models: DPT [4], AdaTrace [5]
- Ad-hoc models
  - mixzone [6]
  - dummy [7]

## Source Codes
We would like to thank the authors for kindly providing the publicly-available runable source code:
- [W4M](https://kdd.isti.cnr.it/W4M/)
- [AdaTrace](https://github.com/git-disl/AdaTrace)
- DPT: we obtained it directly from the authors but don't have the right to make it public here. 
- GLOVE, KLT, mixzone, dummy: implemented by ourselves in Java and provided in this repository.

## Environment
Tested in CentOS Linux and MacOS Monterey (jdk 17.0.1)

## Datasets
[T-Drive](https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/) and [Geolife](https://www.microsoft.com/en-us/download/details.aspx?id=52367) are publicly available datasets provided by Microsoft.
A small subset of T-Drive is provided in this project for testing.

## Project Structure
    Testing/                                  
    ├── BeijingRoadNetworkInfo/                -- the road network information, including road verties/edges and all Beijing POIs
    ├── formal_inputs/                         -- two small sets of T-Drive data have been uploaded here:
                                                  one contains 100 objects' total data ("T-Drive_object/100.csv")
                                                  the other contains 1000 objects' down-sampling data ("T-Drive_sample/3600.csv")
                                                  some objects could be discarded due to too few data points
    src/                                      
    ├── resources/config.properties            -- the program will read parameters here (modify it according to your requirements)
    ├── models/Main.java                       -- the entry of the program
    ├── models/                                -- four models: mixzone, dummy, GLOVE, KLT
    ├── shared/                                -- some useful functions/classes shared by privacy models
    ├── spatial/                               -- spatial structures used in this project
    ├── evaluation/                            -- the evaluation metrics (coming soon)
    
    libs                                       -- some external library packages that are necessary to be included in the program
    ...

## References
[1] O. Abul, F. Bonchi, and M. Nanni, “Anonymization of moving objects databases by clustering and perturbation,” Inf. Syst., vol. 35, no. 8, pp. 884–910, 2010.

[2] M. Gramaglia and M. Fiore, “Hiding mobile traffic fingerprints with GLOVE,” in Proc. CoNEXT. ACM, 2015, pp. 26:1–26:13.

[3] Z. Tu, K. Zhao, F. Xu, Y. Li, L. Su, and D. Jin, “Protecting trajectory from semantic attack considering k-anonymity, l-diversity, and t-closeness,” IEEE Trans. Netw. and Service Management, vol. 16, no. 1, pp. 264–278, 2019

[4] X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, and D. Srivastava, “DPT: differentially private trajectory synthesis using hierarchical reference systems,” Proc. VLDB, vol. 8, no. 11, pp. 1154–1165, 2015.

[5] M. E. Gursoy, L. Liu, S. Truex, L. Yu, and W. Wei, “Utility-aware synthesis of differentially private and attack-resilient location traces,” in Proc. SIGSAC. ACM, 2018, pp. 196–211

[6] X. Liu, H. Zhao, M. Pan, H. Yue, X. Li, and Y. Fang, “Traffic-aware multiple mix zone placement for protecting location privacy,” in Proc. INFOCOM. IEEE, 2012, pp. 972–980.

[7] X. Liu, J. Chen, X. Xia, C. Zong, R. Zhu, and J. Li, “Dummy-based trajectory privacy protection against exposure location attacks,” in Proc. WISA, ser. Lecture Notes in Computer Science, vol. 11817. Springer, 2019, pp. 368–381

## Citation

If you find our library or the experimental results useful, please kindly cite the following paper:
```
@ARTICLE{jin2023,
  author={Jin, Fengmei and Hua, Wen and Francia, Matteo and Chao, Pingfu and Orlowska, Maria E and Zhou, Xiaofang},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing}, 
  year={2023},
  volume={35},
  number={6},
  pages={5577-5596},
  doi={10.1109/TKDE.2022.3174204}
}
```

Please feel free to contact fengmei.jin@uq.edu.au if encountering any unexpected issues in this project.

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