P-GENT: Privacy-preserving GEocoding of Non-geotagged Tweets

Shuo Wang, Richard Sinnott, Surya Nepal

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

1 Citation (Scopus)

Abstract

With the widespread proliferation of location-aware devices and social media applications, more and more people share information on location-based social networks such as Twitter. Such data can be beneficial to better plan and manage individual's activities and other social applications, e.g., location-based advertisement or recommendation. However, only a very small proportion of tweets are geotagged due to privacy concerns or lack of underlying positioning infrastructures. Hence it is meaningful to estimate the geographic information for non-geotagged tweets, i.e., geocoding, which can help to improve the applicability and utility of social media data. Contrary to existing geocoding approaches, this paper aims at the privacy risk and providing a fine-grained estimation. In this paper, we propose Privacy-preserving GEocoding of Non-geotagged Tweets (P-GENT) for geocoding non-geotagged tweets with fine-grained estimation whilst protecting privacy. Our approach estimates the geographic location of a non-geotagged tweet based on the similarities between the content of the tweet and the keyword lists of detected local events form the archived geo-tagged tweets during the same time period. This approach implements a spatio-temporal clustering algorithm to discover local events with a fine-grained granularity and an important keyword extraction mechanism to describe the detected local event. In addition, a density-seed discovery approach is used to reduce the sparseness of geo-tagged tweets and the time complexity of clustering approach. The experimental evaluation with real-world data demonstrates that our approach has at most 92% precision for one timeslot and 33-43% precision remained for all time slots after using privacy-preserving mechanisms.

Original languageEnglish
Title of host publicationProceedings - The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - The 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018) - 2018 IEEE Trustcom/BigDataSE
EditorsKim-Kwang Raymond Choo, Yongxin Zhu, Zongming Fei, Bhavani Thuraisingham, Yang Xiang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages972-983
Number of pages12
ISBN (Electronic)9781538643884, 9781538643877
ISBN (Print)9781538643891
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2018 - New York, United States of America
Duration: 31 Jul 20183 Aug 2018
Conference number: 17th
http://www.cloud-conf.net/trustcom18/ (Conference Website)
https://ieeexplore.ieee.org/xpl/conhome/8454845/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2018
Abbreviated titleTrustCom 2018
CountryUnited States of America
CityNew York
Period31/07/183/08/18
Internet address

Keywords

  • Differential privacy
  • event detection
  • location estimation
  • spatio temporal clustering

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