Location K-anonymity in indoor spaces

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• 作者：Joon-Seok Kim ; Ki-Joune Li
• 刊名：GeoInformatica
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：20
• 期：3
• 页码：415-451
• 全文大小：4,295 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Geography
  Geographical Information Systems and Cartography
  Data Structures, Cryptology and Information Theory
  Computer Science, general
  Information Storage and Retrieval
  Multimedia Information Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7624
• 卷排序：20

文摘

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.KeywordsLocation \(\mathcal {K}\)-anonymityl-diversityPrivacyHierarchical graphIndoor spacek-NN query