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Sharing and generating privacy-preserving spatio-temporal data using real-world knowledge
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Cunningham, Teddy (2022) Sharing and generating privacy-preserving spatio-temporal data using real-world knowledge. In: 23rd IEEE International Conference on Mobile Data Management (MDM), Paphos, Cyprus ; Online, 6-9 Jul 2022. Published in: 2022 23rd IEEE International Conference on Mobile Data Management (MDM) ISBN 9781665451765. doi:10.1109/MDM55031.2022.00074 ISSN 2375-0324.
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Official URL: https://doi.org/10.1109/MDM55031.2022.00074
Abstract
Privacy-preserving spatio-temporal data sharing is vital in many machine learning and analysis tasks, such as managing disease spread or tailoring public services to a population’s travel patterns. Current methods for data release are insufficiently accurate to provide meaningful utility, and they carry a high risk of deanonymization or membership inference attacks. These limitations and public concern over privacy and data protection has limited the extent to which data is shared. This work presents approaches generating and publishing spatio-temporal data, such as geographic locations and trajectories, with differential privacy. In the first solution, differentially private spatial data is generated using kernel density estimation and a road network-aware approach. In the second solution, a local differentially private mechanism is developed by perturbing hierarchically-structured, overlapping n-grams of trajectory data. Both of the solutions incorporate publicly available information, such as the road network or categories of places of interests, to enhance the utility of the output data without negatively affecting privacy or efficiency. Experiments with real-world data demonstrate that the private data can perform as well as the non-private data in a range of practical data science tasks.
| Item Type: | Conference Item (Paper) | |||||||||||||||
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| Subjects: | J Political Science > JC Political theory Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Machine learning , Data reduction, Data protection -- Mathematics, Privacy, Right of | |||||||||||||||
| Journal or Publication Title: | 2022 23rd IEEE International Conference on Mobile Data Management (MDM) | |||||||||||||||
| Publisher: | IEEE | |||||||||||||||
| ISBN: | 9781665451765 | |||||||||||||||
| ISSN: | 2375-0324 | |||||||||||||||
| Official Date: | 25 August 2022 | |||||||||||||||
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| DOI: | 10.1109/MDM55031.2022.00074 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| Date of first compliant deposit: | 9 June 2022 | |||||||||||||||
| Date of first compliant Open Access: | 10 June 2022 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||||||||
| Title of Event: | 23rd IEEE International Conference on Mobile Data Management (MDM) | |||||||||||||||
| Type of Event: | Conference | |||||||||||||||
| Location of Event: | Paphos, Cyprus ; Online | |||||||||||||||
| Date(s) of Event: | 6-9 Jul 2022 | |||||||||||||||
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