, Volume 20, Issue 3, pp 415–451 | Cite as

Location K-anonymity in indoor spaces

  • Joon-Seok Kim
  • Ki-Joune LiEmail author


With the expansion of wireless-communication infrastructure and the evolution of indoor positioning technologies, the demand for location-based services (LBS) has been increasing in indoor as well as outdoor spaces. However, we should consider a significant challenge regarding the location privacy for realizing indoor LBS. To avoid violations of location privacy, much research has been performed, and location \(\mathcal {K}\)-anonymity has been intensively studied to blur a user location with a cloaking region involving at least \(\mathcal {K}-1\) locations of other persons. Owing to the differences between indoor and outdoor spaces, it is, however, difficult to apply this approach directly in an indoor space. First, the definition of the distance metric in indoor space is different from that in Euclidean and road-network spaces. Second, a bounding region, which is a general form of an anonymizing spatial region (ASR) in Euclidean space, does not respect the locality property in indoor space, where movement is constrained by building components. Therefore, we introduce the concept of indoor location \(\mathcal {K}\)-anonymity in this paper. Then, we investigate the requirements of ASR in indoor spaces and propose novel methods to determine the ASR, considering hierarchical structures of the indoor space. While indoor ASRs are determined at the anonymizer, we also propose processing methods for r-range queries and k-nearest-neighbor queries at a location-based service provider. We validate our methods with experimental analysis of query-processing performance and resilience against attacks in indoor spaces.


Location \(\mathcal {K}\)-anonymity l-diversity Privacy Hierarchical graph Indoor space k-NN query 



This research was partially supported by a grant(11 High-tech G11) from Architecture & Urban Development Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government, and a grant(14NSIP-B080144-01) from National Land Space Information Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government. This work was partially supported by BK21PLUS, Creative Human Resource Development Program for IT Convergence.


  1. 1.
    Afyouni I, Ray C, Claramunt C (2012) Spatial models for context-aware indoor navigation systems: A survey. J Spat Inf Sci 4(1):85–123Google Scholar
  2. 2.
    Gedik B, Liu L (2005) Location privacy in mobile systems: a personalized anonymization model. In: ICDCS, pp 620–629Google Scholar
  3. 3.
    Ghinita G, Kalnis P, Khoshgozaran A, Shahabi C, Tan KL (2008) Private queries in location based services: anonymizers are not necessary. In: SIGMOD Conference, pp 121–132. doi: 10.1145/1376616.1376631
  4. 4.
    Ghinita G, Zhao K, Papadias D, Kalnis P (2010) A reciprocal framework for spatial k-anonymity. Inf Syst 35(3):299–314. doi: 10.1016/ CrossRefGoogle Scholar
  5. 5.
    Gkoulalas-Divanis A, Kalnis P, Verykios VS (2010) Providing k-anonymity in location based services. SIGKDD Explor 12(1):3–10CrossRefGoogle Scholar
  6. 6.
    Gruteser M, Grunwald D (2003) Anonymous usage of location-based services through spatial and temporal cloaking. In: MOBISYS, pp 31–42Google Scholar
  7. 7.
    Hagedorn B, Trapp M, Glander T, Dollner J (2009) Towards an indoor level-of-detail model for route visualization. In: MDM, pp 692–697Google Scholar
  8. 8.
    Kalnis P, Ghinita G, Mouratidis K, Papadias D (2007) Preventing location-based identity inference in anonymous spatial queries. IEEE Trans Knowl Data Eng 19(12):1719–1733CrossRefGoogle Scholar
  9. 9.
    Khoshgozaran A, Shahabi C (2007) Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In: SSTD, pp 239–257Google Scholar
  10. 10.
    Khoshgozaran A, Shahabi C (2010) A taxonomy of approaches to preserve location privacy in location-based services. Int J Comput Sci Eng 5 (2):86–96. doi: 10.1504/IJCSE.2010.036819 CrossRefGoogle Scholar
  11. 11.
    Khoshgozaran A, Shahabi C, Shirani-Mehr H (2011) Location privacy: going beyond k-anonymity, cloaking and anonymizers. Knowl Inf Syst 26(3):435–465. doi: 10.1007/s10115-010-0286-z CrossRefGoogle Scholar
  12. 12.
    Kim JS, Han Y, Li KJ (2012) K-anonymity in indoor spaces through hierarchical graphs. In: Proceedings of the fourth ACM SIGSPATIAL international workshop on indoor spatial awareness, pp 21–28. doi: 10.1145/2442616.2442622
  13. 13.
    Kim YK, Hossain A, Hossain AA, Chang JW (2013) Hilbert-order based spatial cloaking algorithm in road network. Concurrency Comput Prac Exp 25 (1):143–158. doi: 10.1002/cpe.2844 CrossRefGoogle Scholar
  14. 14.
    Lee J, Li KJ, Zlatanova S, Kolbe TH, Nagel C, Becker T (2014) Ogc indoorgml v.1.0, accessed: 2015-02-25.
  15. 15.
    Li KJ (2008) A new notion of space. In: W2GIS, pp 1–3Google Scholar
  16. 16.
    Li PY, Peng WC, Wang TW, Ku WS, Xu J, Hamilton JA Jr (2008) A cloaking algorithm based on spatial networks for location privacy. In: SUTC, pp 90–97. doi: 10.1109/SUTC.2008.56
  17. 17.
    Lozano-Pérez T, Wesley MA (1979) An algorithm for planning collision-free paths among polyhedral obstacles. Commun ACM 22(10):560–570. doi: 10.1145/359156.359164 CrossRefGoogle Scholar
  18. 18.
    Lu H, Cao X, Jensen CS (2012) A foundation for efficient indoor distance-aware query processing. In: ICDE, pp 438–449. doi: 10.1109/ICDE.2012.44
  19. 19.
    Mokbel MF, Chow CY, Aref WG (2006) The new casper: Query processing for location services without compromising privacy. In: VLDB, pp 763–774Google Scholar
  20. 20.
    Mouratidis K, Yiu ML (2010) Anonymous query processing in road networks. IEEE Trans Knowl Data Eng 22(1):2–15. doi: 10.1109/TKDE.2009.48 CrossRefGoogle Scholar
  21. 21.
    Papadopoulos S, Bakiras S, Papadias D (2010) Nearest neighbor search with strong location privacy. Proc VLDB Endow 3(1–2):619–629. doi: 10.14778/1920841.1920920 CrossRefGoogle Scholar
  22. 22.
    Richter K, Winter S, Ruetschi U (2009) Constructing hierarchical representations of indoor spaces. In: MDM, pp 686–691Google Scholar
  23. 23.
    Stoel E, Schoder K, Ohlbach HJ (2008) Applying hierarchical graphs to pedestrian indoor navigation. In: ACM SIGSpatial GIS, pp 54:1–54:4Google Scholar
  24. 24.
    Wang T, Liu L (2009) Privacy-aware mobile services over road networks. Proc VLDB Endow 2(1): 1042–1053CrossRefGoogle Scholar
  25. 25.
    Xie X, Lu H, Pedersen TB (2013) Efficient distance-aware query evaluation on indoor moving objects. In: ICDE, pp 434–445Google Scholar
  26. 26.
    Xue J, Liu X, Yang X, Wang B (2010) Protecting location privacy using cloaking subgraphs on road network. In: WISA, pp 65–68Google Scholar
  27. 27.
    Xue M, Kalnis P, Pung H (2009) Location diversity: enhanced privacy protection in location based services. In: LoCA, pp 70–87Google Scholar
  28. 28.
    Yang B, Lu H, Jensen CS (2010) Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space. In: EDBT, pp 335–346. doi: 10.1145/1739041.1739083
  29. 29.
    Yiu ML, Jensen CS, Huang X, Lu H (2008) Spacetwist: managing the trade-offs among location privacy, query performance, and query accuracy in mobile services. In: ICDE, pp 366–375. doi: 10.1109/ICDE.2008.4497445
  30. 30.
    Yuan W, Schneider M (2010) Supporting continuous range queries in indoor space. In: MDM , pp 209–214Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPusan National UniversityBusanSouth Korea

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