A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910538833854464 |
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| author | Bhadani, Rahul |
| author_facet | Bhadani, Rahul |
| contents | With low-cost computing devices, improved sensor technology, and the proliferation of data-driven algorithms, we have more data than we know what to do with. In transportation, we are seeing a surge in spatiotemporal data collection. At the same time, concerns over user privacy have led to research on differential privacy in applied settings. In this paper, we look at some recent developments in differential privacy in the context of spatiotemporal data. Spatiotemporal data contain not only features about users but also the geographical locations of their frequent visits. Hence, the public release of such data carries extreme risks. To address the need for such data in research and inference without exposing private information, significant work has been proposed. This survey paper aims to summarize these efforts and provide a review of differential privacy mechanisms and related software. We also discuss related work in transportation where such mechanisms have been applied. Furthermore, we address the challenges in the deployment and mass adoption of differential privacy in transportation spatiotemporal data for downstream analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research Bhadani, Rahul Cryptography and Security Computers and Society Machine Learning Methodology With low-cost computing devices, improved sensor technology, and the proliferation of data-driven algorithms, we have more data than we know what to do with. In transportation, we are seeing a surge in spatiotemporal data collection. At the same time, concerns over user privacy have led to research on differential privacy in applied settings. In this paper, we look at some recent developments in differential privacy in the context of spatiotemporal data. Spatiotemporal data contain not only features about users but also the geographical locations of their frequent visits. Hence, the public release of such data carries extreme risks. To address the need for such data in research and inference without exposing private information, significant work has been proposed. This survey paper aims to summarize these efforts and provide a review of differential privacy mechanisms and related software. We also discuss related work in transportation where such mechanisms have been applied. Furthermore, we address the challenges in the deployment and mass adoption of differential privacy in transportation spatiotemporal data for downstream analyses. |
| title | A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research |
| topic | Cryptography and Security Computers and Society Machine Learning Methodology |
| url | https://arxiv.org/abs/2407.15868 |