Cunningham, Teddy, Cormode, Graham and Ferhatosmanoglu, Hakan (2021) Privacy-preserving synthetic location data in the real world. In: SSTD '21: 17th International Symposium on Spatial and Temporal Databases, Virtual, 23-25 Aug 2021. Published in: Proceedings of International Symposium on Spatial and Temporal Databases, 2021 pp. 23-33. ISBN 9781450384254. doi:10.1145/3469830.3470893

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Abstract

Sharing sensitive data is vital in enabling many modern data analysis and machine learning tasks. However, current methods for data release are insufficiently accurate or granular to provide meaningful utility, and they carry a high risk of deanonymization or membership inference attacks. In this paper, we propose a differentially private synthetic data generation solution with a focus on the compelling domain of location data. We present two methods with high practical utility for generating synthetic location data from real locations, both of which protect the existence and true location of each individual in the original dataset. Our first, partitioning-based approach introduces a novel method for privately generating point data using kernel density estimation, in addition to employing private adaptations of classic statistical techniques, such as clustering, for private partitioning. Our second, network-based approach incorporates public geographic information, such as the road network of a city, to constrain the bounds of synthetic data points and hence improve the accuracy of the synthetic data. Both methods satisfy the requirements of differential privacy, while also enabling accurate generation of synthetic data that aims to preserve the distribution of the real locations. We conduct experiments using three large-scale location datasets to show that the proposed solutions generate synthetic location data with high utility and strong similarity to the real datasets. We highlight some practical applications for our work by applying our synthetic data to a range of location analytics queries, and we demonstrate that our synthetic data produces near-identical answers to the same queries compared to when real data is used. Our results show that the proposed approaches are practical solutions for sharing and analyzing sensitive location data privately.

Item Type:	Conference Item (Paper)
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title:	Proceedings of International Symposium on Spatial and Temporal Databases, 2021
Publisher:	ACM
ISBN:	9781450384254
Official Date:	23 August 2021
Dates:	Date Event 23 August 2021 Published 23 August 2021 Available 14 June 2021 Accepted
Page Range:	pp. 23-33
DOI:	10.1145/3469830.3470893
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Cunningham, Teddy, Cormode, Graham and Ferhatosmanoglu, Hakan (2021) Privacy-preserving synthetic location data in the real world. In: 17th International Symposium on Spatial and Temporal Databases, Virtual conference, 23-25 Aug 2021. Published in: Proceedings of International Symposium on Spatial and Temporal Databases, 2021 pp. 23-33. ISBN 9781450384254. http://doi.acm.org/10.1145/3469830.3470893
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	24 June 2021
Date of first compliant Open Access:	23 August 2021
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/L016400/1 UK Engineering and Physical Sciences Research Council (EPSRC) UNSPECIFIED ERC-2014-CoG 647557 European Research Council UNSPECIFIED EP/N510129/1 Alan Turing Institute UNSPECIFIED
Conference Paper Type:	Paper
Title of Event:	SSTD '21: 17th International Symposium on Spatial and Temporal Databases
Type of Event:	Conference
Location of Event:	Virtual
Date(s) of Event:	23-25 Aug 2021
Related URLs:	Publisher

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