Spatial Bloom Filters: Enabling Privacy in Location-Aware Applications View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015-03-28

AUTHORS

Paolo Palmieri , Luca Calderoni , Dario Maio

ABSTRACT

The wide availability of inexpensive positioning systems made it possible to embed them into smartphones and other personal devices. This marked the beginning of location-aware applications, where users request personalized services based on their geographic position. The location of a user is, however, highly sensitive information: the user’s privacy can be preserved if only the minimum amount of information needed to provide the service is disclosed at any time. While some applications, such as navigation systems, are based on the users’ movements and therefore require constant tracking, others only require knowledge of the user’s position in relation to a set of points or areas of interest. In this paper we focus on the latter kind of services, where location information is essentially used to determine membership in one or more geographic sets. We address this problem using Bloom Filters (BF), a compact data structure for representing sets. In particular, we present an extension of the original Bloom filter idea: the Spatial Bloom Filter (SBF). SBF’s are designed to manage spatial and geographical information in a space efficient way, and are well-suited for enabling privacy in location-aware applications. We show this by providing two multi-party protocols for privacy-preserving computation of location information, based on the known homomorphic properties of public key encryption schemes. The protocols keep the user’s exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user. More... »

PAGES

16-36

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-16745-9_2

DOI

http://dx.doi.org/10.1007/978-3-319-16745-9_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017071746


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