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Real-world trajectory sharing with local differential privacy
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Cunningham, Teddy, Cormode, Graham, Ferhatosmanoglu, Hakan and Srivastava, Divesh (2021) Real-world trajectory sharing with local differential privacy. In: VLDB Endowment, Virtual, 16-20 Aug 2021. Published in: Proceedings of the VLDB Endowment, 14 (11). pp. 2283-2295. doi:10.14778/3476249.3476280 ISSN 2150-8097.
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Official URL: https://doi.org/10.14778/3476249.3476280
Abstract
Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping n-grams (i.e., contiguous subsequences of length n) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories.Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.
Item Type: | Conference Item (Paper) | ||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Data protection, Electronic data processing, Location-based services | ||||||||||||
Journal or Publication Title: | Proceedings of the VLDB Endowment | ||||||||||||
Publisher: | ACM | ||||||||||||
ISSN: | 2150-8097 | ||||||||||||
Official Date: | 2021 | ||||||||||||
Dates: |
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Volume: | 14 | ||||||||||||
Number: | 11 | ||||||||||||
Page Range: | pp. 2283-2295 | ||||||||||||
DOI: | 10.14778/3476249.3476280 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 21 July 2021 | ||||||||||||
Date of first compliant Open Access: | 21 July 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||
Title of Event: | VLDB Endowment | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Virtual | ||||||||||||
Date(s) of Event: | 16-20 Aug 2021 | ||||||||||||
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