Privacy-preserving mobility monitoring using sketches of stationary sensor readings

Michael Kamp, Christine Kopp, Michael Mock, Mario Boley, Michael May

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)

Abstract

Two fundamental tasks of mobility modeling are (1) to track the number of distinct persons that are present at a location of interest and (2) to reconstruct flows of persons between two or more different locations. Stationary sensors, such as Bluetooth scanners, have been applied to both tasks with remarkable success. However, this approach has privacy problems. For instance, Bluetooth scanners store the MAC address of a device that can in principle be linked to a single person. Unique hashing of the address only partially solves the problem because such a pseudonym is still vulnerable to various linking attacks. In this paper we propose a solution to both tasks using an extension of linear counting sketches. The idea is to map several individuals to the same position in a sketch, while at the same time the inaccuracies introduced by this overloading are compensated by using several independent sketches. This idea provides, for the first time, a general set of primitives for privacy preserving mobility modeling from Bluetooth and similar address-based devices.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2013 Prague, Czech Republic, September 23-27, 2013 Proceedings, Part III
EditorsHendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Železný
Place of PublicationBerlin Germany
PublisherSpringer
Pages370-386
Number of pages17
ISBN (Electronic)9783642409943
ISBN (Print)9783642409936
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases 2013 - Prague, Czech Republic
Duration: 23 Sep 201327 Sep 2013
http://www.ecmlpkdd2013.org/
https://link.springer.com/book/10.1007/978-3-642-40988-2 (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8190
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases 2013
Abbreviated titleECML PKDD 2013
Country/TerritoryCzech Republic
CityPrague
Period23/09/1327/09/13
Internet address

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