This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.

/pdf/the-new-casper-query-processing-for-location-services-1kit6ixqvn.pdf

The new Casper: query processing for location services without compromising privacy

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user's perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.

/pdf/a-model-for-enriching-trajectories-with-semantic-1s00xjclgc.pdf

A model for enriching trajectories with semantic geographical information

This document presents a method to design scalable and representative moving object data (MOD) and two sets of queries for benchmarking spatio-temporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a simulation scenario, where the positions of a sample of vehicles are observed for an arbitrary period of time within the street network of Berlin. We demonstrate the data generator's extensibility by showing how to achieve more natural movement generation patterns, and how to disturb the vehicles' positions to create noisy data. As an application and for reference, we also present first benchmarking results for the Secondo DBMS. Whereas the benchmark focuses on range queries, we demonstrate its ability to incorporate new future classes of queries by presenting a preliminary extension handling various nearest neighbour queries. Such a benchmark is useful in several ways: It provides well-defined data sets and queries for experimental evaluations; it simplifies experimental repeatability; it emphasizes the development of complete systems; it points out weaknesses in existing systems motivating further research. Moreover, the BerlinMOD benchmark allows one to compare different representations of the same moving objects.

BerlinMOD: a benchmark for moving object databases

The wide spread of location-based services results in a strong market for location-detection devices (e.g., GPS-like devices, RFIDs, handheld devices, and cellular phones). Examples of location-based services include location-aware emergency service, location-based advertisement, live traffic reports, and location-based store finder. However, location-detection devices pose a major privacy threat on its users where it transmits private information (i.e., the location) to the server who may be untrustworthy. The existing model of location-based applications trades service with privacy where if a user wants to keep her private location information, she has to turn off her location-detection device, i.e., unsubscribe from the service. This paper tackles this model in a way that protects the user privacy while keeping the functionality of location-based services. The main idea is to employ a trusted third party, the Location Anonymizer, that expands the user location into a spatial region such that: (1) The exact user location can lie anywhere in the spatial region, and (2) There are k other users within the expanded spatial region so that each user is k-anonymous. The location-based database server is equipped with additional functionalities that support spatio-temporal queries based on the spatial region received from the location anonymizer rather than the exact point location received from the user.

/pdf/towards-privacy-aware-location-based-database-servers-3jlnyopgaa.pdf

Towards Privacy-Aware Location-Based Database Servers

For many years trajectory data have been treated as sequences of space-time points or stops and moves. However, with the explosion of the Internet of Things (IoT) and the flood of Big Data generated on the Internet, like weather channels and social network interactions that can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions. The main challenge is how to integrate all these information with trajectories. In this paper we introduce a new concept of trajectory, called multiple aspect trajectory, propose a robust conceptual and logical data model that supports a vaste range of applications, and differently from state-of-the-art methods we propose an efficient storage solution for efficient multiple aspect trajectory queries. The main strength of the model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario and compare its query performance against SECONDO extension for symbolic trajectories.

MASTER: A multiple aspect view on trajectories

The goal of SECONDO is to provide a "generic" database system frame that can be filled with implementations of various DBMS data models. SECONDO was intended originally as a platform for implementing and experimenting with new kinds of data models, especially to support spatial, spatio-temporal, and graph database models. We now feel, SECONDO has a clean architecture, and it strike a reasonable balance between simplicity and sophistication. Since all the source code is accessible and to a large extent comprehensible for students, we believe it is also an excellent tool for teaching database architecture and implementation concepts. SECONDO runs on Windows, Linux, and Solaris platforms, and consists of three major components SECONDO kernel, optimizer, and graphical user interface.

SECONDO: an extensible DBMS platform for research prototyping and teaching

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SECONDO: an extensible DBMS platform for research prototyping and teaching