Anonymizing continuous queries with delay-tolerant mix-zones over road networks

This paper presents a delay-tolerant mix-zone framework for protecting the location privacy of mobile users against continuous query correlation attacks. First, we describe and analyze the continuous query correlation attacks (CQ-attacks) that perform query correlation based inference to break the anonymity of road network-aware mix-zones. We formally study the privacy strengths of the mix-zone anonymization under the CQ-attack model and argue that spatial cloaking or temporal cloaking over road network mix-zones is ineffective and susceptible to attacks that carry out inference by combining query correlation with timing correlation (CQ-timing attack) and transition correlation (CQ-transition attack) information. Next, we introduce three types of delay-tolerant road network mix-zones (i.e.; temporal, spatial and spatio-temporal) that are free from CQ-timing and CQ-transition attacks and in contrast to conventional mix-zones, perform a combination of both location mixing and identity mixing of spatially and temporally perturbed user locations to achieve stronger anonymity under the CQ-attack model. We show that by combining temporal and spatial delay-tolerant mix-zones, we can obtain the strongest anonymity for continuous queries while making acceptable tradeoff between anonymous query processing cost and temporal delay incurred in anonymous query processing. We evaluate the proposed techniques through extensive experiments conducted on realistic traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that the proposed techniques offer high level of anonymity and attack resilience to continuous queries. © 2013 Springer Science+Business Media New York

Investigating the use of semantic technologies in spatial mapping applications

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend

Onion Curve: A Space Filling Curve with Near-Optimal Clustering

Space filling curves (SFCs) are widely used in the design of indexes for spatial and temporal data. Clustering is a key metric for an SFC, that measures how well the curve preserves locality in moving from higher dimensions to a single dimension. We present the {\em onion curve}, an SFC whose clustering performance is provably close to optimal for the cube and near-cube shaped query sets, irrespective of the side length of the query. We show that in contrast, the clustering performance of the widely used Hilbert curve can be far from optimal, even for cube-shaped queries. Since the clustering performance of an SFC is critical to the efficiency of multi-dimensional indexes based on the SFC, the onion curve can deliver improved performance for data structures involving multi-dimensional data.Comment: The short version is published in ICDE 1