Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the fc-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k - 1 other individuals. In this paper we study the problem of anonymity preserving data publishing in moving objects databases. We propose a novel concept of k-anonymity based on co-localization that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and positioning systems (e.g., GPS) imprecision, the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius delta represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,delta) -anonymity for moving objects databases. We first characterize the (k, delta)-anonymity problem and discuss techniques to solve it. Then we focus on the most promising technique by the point of view of information preservation, namely space translation. We develop a suitable measure of the information distortion introduced by space translation, and we prove that the problem of achieving (k,delta) -anonymity by space translation with minimum distortion is NP-hard. Faced with the hardness of our problem we propose a greedy algorithm based on clustering and enhanced with ad hoc pre-processing and outlier removal techniques. The resulting method, named NWA (Never Walk .Alone), is empirically evaluated in terms of data quality and efficiency. Data quality is assessed both by means of objective measures of information distortion, and by comparing the results of the same spatio-temporal range queries executed on the original database and on the (k, delta)-anonymized one. Experimental results show that for a wide range of values of delta and k, the relative error introduced is kept low, confirming that NWA produces high quality (k, delta)-anonymized data.

/pdf/never-walk-alone-uncertainty-for-anonymity-in-moving-objects-4rdtctkjxy.pdf

Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases

Spatiotemporal applications, such as fleet management and air traffic control, involving continuously moving objects are increasingly at the focus of research efforts. The representation of the continuously changing positions of the objects is fundamentally important in these applications. This paper reports on on-going research in the representation of the positions of moving-point objects. More specifically, object positions are sampled using the Global Positioning System, and interpolation is applied to determine positions in-between the samples. Special attention is given in the representation to the quantification of the position uncertainty introduced by the sampling technique and the interpolation. In addition, the paper considers the use for query processing of the proposed representation in conjunction with indexing. It is demonstrated how queries involving uncertainty may be answered using the standard filter-and-refine approach known from spatial query processing.

/pdf/capturing-the-uncertainty-of-moving-object-representations-vcxrbq4tot.pdf

Capturing the Uncertainty of Moving-Object Representations

We show how to index mobile objects in one and two dimensions using efficient dynamic external memory data structures. The problem is motivated by real life applications in traffic monitoring, intelligent navigation and mobile communications domains. For the l-dimensional case, we give (i) a dynamic, external memory algorithm with guaranteed worst case performance and linear space and (ii) a practical approximation algorithm also in the dynamic, external memory setting, which has linear space and expected logarithmic query time. We also give an algorithm with guaranteed logarithmic query time for a restricted version of the problem. We present extensions of our techniques to two dimensions. In addition we give a lower bound on the number of I/O’s needed to answer the d-dimensional problem. Initial experimental results and comparisons to traditional indexing approaches are also included.

/pdf/on-indexing-mobile-objects-3ozmvusix7.pdf

On indexing mobile objects

This article addresses the problem of managing Moving Objects Databases (MODs) which capture the inherent imprecision of the information about the moving object's location at a given time. We deal systematically with the issues of constructing and representing the trajectories of moving objects and querying the MOD. We propose to model an uncertain trajectory as a three-dimensional (3D) cylindrical body and we introduce a set of novel but natural spatio-temporal operators which capture the uncertainty and are used to express spatio-temporal range queries. We devise and analyze algorithms for processing the operators and demonstrate that the model incorporates the uncertainty in a manner which enables efficient querying, thus striking a balance between the modeling power and computational efficiency. We address some implementation aspects which we experienced in our DOMINO project, as a part of which the operators that we introduce have been implemented. We also report on some experimental observations of a practical relevance.

Managing uncertainty in moving objects databases

Indexing moving points

Consider a database that represents the location of moving objects, such as taxi-cabs (typical query: "retrieve the cabs that are currently within 1 mile of 33 Michigan Ave., Chicago"), or objects in a battle-field. Existing database management systems (DBMSs) are not well equipped to handle continuously changing data, such as the position of moving objects, since data is assumed to be constant unless it is explicitly modified. In this paper, we address position-update policies and imprecision. Assuming that the actual position of a moving object m deviates from the position computed by the DBMS, when should m update its position in the database in order to eliminate the deviation? Furthermore, how can the DBMS provide a bound on the error (i.e. the deviation) when it replies to a query, such as: "what is the current position of m?" We propose a cost-based approach to update policies that answers both questions. We develop several update policies and analyze them theoretically and experimentally.

Cost and imprecision in modeling the position of moving objects

AIDA is a distributed data management (DDM) front-end system which runs on top of existing databases stored in different data management systems (DBMSs). It appears to the user to be a distributed DBMS, although many of the DBMS functions are actually provided by the underlying DBMS. It currently runs above three relational DBMSs, the IDM database machine connected to a VAX, Ingres on two Suns, and Mistress on a third Sun. One of the key components of the AIDA system is Mermaid which provides query optimization, networking, and system control. Mermaid required research into query optimization where the costs of operations on the different processors and DBMSs is variable, the network cost is variable, and capabilities of the different DBMSs is variable. We extended existing distributed query optimization algorithms to support replicated and fragmented relations. In this paper, we discuss our experiences with the development and testing of the Mermaid system and with the integration of Mermaid with the AIDA translator. We discuss the types of commands, the system controller, and our experiences with distributed debugging. Then we give the results of our initial performance tests.

Mermaid — Experiences with network operation

A new hybrid algorithm is proposed for well-formed path problems including the transitive closure problem. The CPU time for computation is O(ne), and blocking technique is incorporated to reduce the disk I/O cost in disk-resident environment. The new features of the new algorithm are that only parents sets instead of descendant sets are loaded in from disk, and the computation can be parallelized efficiently. Simulation results show that our algorithm is superior to other existing algorithms in sequential computation, and that linear speedup is achieved in parallel computation. >

A hybrid transitive closure algorithm for sequential and parallel processing

In this paper we propose a data model for representing moving objects with uncertain positions in database systems. It is called the Moving Objects Spatio-Temporal (MOST) data model. We also propose Future Temporal Logic (FTL) as the query language for the MOST model, and devise an algorithm for processing FTL queries in MOST.

Querying the uncertain position of moving objects

Querying the Uncertain Position of Moving Objects.

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