Privacy-protected place of activity mining on big location data

Shuo Wang, Richard Sinnott, Surya Nepal

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

1 Citation (Scopus)

Abstract

People always spend their time at a few important locations for various activities in groups during specific time slots, called place of activity (POA), e.g., resting at home among family members during night and working at office among colleagues during work time. Inferring such places is significant for not only the precise advertising on the commercial aspect but the identifying rallies or meetings among a group of people and tracking of the target individuals on the aspect of public security, e.g., locating and tracking suspected terrorists for anti-terrorist work. However, it is a challenge to map from big location data to places of activity due to the volume and complexity whilst giving rise to privacy concerns, e.g., personally important place mining. In the paper, a method for POA mining on big location data is proposed, named P-PAM, aiming at big data analytics and privacy concerns. We use a clustering algorithm to discover the place of activity, then adopt location entropy as reference of user diversity and take into account temporal variation, to infer place of activity. Further, robust privacy-preserving mechanisms under differential privacy are embedded into clustering results and location entropy evaluation that accesses to raw location data. We demonstrate the utility of our proposed approach with large-scale location datasets derived from geo-referenced social media. The experimental results suggest that the POA mining approach can successfully scale to big data scenarios whilst preserving individual user privacy.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1101-1108
Number of pages8
ISBN (Electronic)9781538627143, 9781538627150
ISBN (Print)9781538627167
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Conference on Big Data (Big Data) 2017 - Boston, United States of America
Duration: 11 Dec 201714 Dec 2017
http://cci.drexel.edu/bigdata/bigdata2017/
https://ieeexplore.ieee.org/xpl/conhome/8241556/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Big Data (Big Data) 2017
Abbreviated titleIEEE BigData 2017
CountryUnited States of America
CityBoston
Period11/12/1714/12/17
Internet address

Keywords

  • big data
  • clustering
  • differential privacy
  • Location privacy
  • spatiotemporal data

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