2012 DifferentiallyPrivateTransitDat

• (Chen et al., 2012) ⇒ Rui Chen, Benjamin C.M. Fung, Bipin C. Desai, and Nériah M. Sossou. (2012). “Differentially Private Transit Data Publication: A Case Study on the Montreal Transportation System.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339564

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Differentially+Private+Transit+Data+Publication%3A+A+Case+Study+on+the+Montreal+Transportation+System
• http://dl.acm.org/citation.cfm?id=2339530.2339564&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; data mining; differential privacy; non-interactive release; security, integrity, and protection; transit data

Abstract

With the wide deployment of smart card automated fare collection (SCAFC) systems, public transit agencies have been benefiting from huge volume of transit data, a kind of sequential data, collected every day. Yet, improper publishing and use of transit data could jeopardize passengers' privacy. In this paper, we present our solution to transit data publication under the rigorous differential privacy model for the Société de transport de Montréal (STM). We propose an efficient data-dependent yet differentially private transit data sanitization approach based on a hybrid-granularity prefix tree structure. Moreover, as a post-processing step, we make use of the inherent consistency constraints of a prefix tree to conduct constrained inferences, which lead to better utility. Our solution not only applies to general sequential data, but also can be seamlessly extended to trajectory data. To our best knowledge, this is the first paper to introduce a practical solution for publishing large volume of sequential data under differential privacy. We examine data utility in terms of two popular data analysis tasks conducted at the STM, namely count queries and frequent sequential pattern mining. Extensive experiments on real-life STM datasets confirm that our approach maintains high utility and is scalable to large datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 DifferentiallyPrivateTransitDat	Benjamin C.M. Fung Rui Chen Bipin C. Desai Nériah M. Sossou			Differentially Private Transit Data Publication: A Case Study on the Montreal Transportation System				10.1145/2339530.2339564		2012