04-07-2012, 01:09 PM
PAM: AN EFFICIENT AND PRIVACY-AWARE MONITORING FRAMEWORK FOR CONTINUOUSLY MOVING OBJECTS
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ABSTRACT
Efficiency and privacy are two fundamental issues in moving object monitoring. This paper proposes a privacy-aware monitoring (PAM) framework that addresses both issues. The framework distinguishes itself from the existing work by being the first to holistically address the issues of location updating in terms of monitoring accuracy, efficiency, and privacy, particularly, when and how mobile clients should send location updates to the server. Based on the notions of safe region and most probable result, PAM performs location updates only when they would likely alter the query results. Furthermore, by designing various client update strategies, the framework is flexible and able to optimize accuracy, privacy, or efficiency. We develop efficient query evaluation/reevaluation and safe region computation algorithms in the framework. The experimental results show that PAM substantially outperforms traditional schemes in terms of monitoring accuracy, CPU cost, and scalability while achieving close-to-optimal communication cost.
OBJECTIVE
This framework proposes two fundamental issues of monitoring moving objects accuracy and privacy when locating the updates in the server with safe region techniques.
PROBLEM DEFINITION
In our framework clients aware of location being updated in the server, for the case of efficiency we derive a rectangular region called safe region to avoid the periodic update. The Location Updater, which helps to compute the safe regions and update the query.
Here, privacy is implemented when clients location as been updated in the Database server. To protect against it, most existing work suggests replacing accurate point locations by bounding boxes to reduce location resolutions
In our monitoring system architecture will be based on the client, application server and database server. The application servers gather monitoring requests and register spatial queries at the database server, which then continuously updates the query results until the queries are deregistered.
LITERATURE REVIEW
In mobile and spatiotemporal databases, monitoring continuous spatial queries over moving objects is needed in numerous applications such as public transportation, logistics, and location-based services. A typical monitoring system consists of a base station, a database server, application servers, and a large number of moving objects (i.e., mobile clients). The database server manages the location information of the objects. The application servers gather monitoring requests and register spatial queries at the database server, which then continuously updates the query results until the queries are deregistered.
The fundamental problem in a monitoring system is when and how a mobile client should send location updates to the server because it determines three principal performance measures of monitoring—accuracy, efficiency, and privacy.
Accuracy means how often the monitored results are correct, and it heavily depends on the frequency and accuracy of location updates.
As for efficiency, two dominant costs are: the wireless communication cost for location updates and the query evaluation cost at the database server, both of which depend on the frequency of location updates.
As for privacy, the accuracy of location updates determines how much the client’s privacy is exposed to the server.
Periodic Update and Deviation Update
Two commonly used updating approaches are periodic update (every client reports its new location at a fixed interval) and deviation update (a client performs an update when its location or velocity changes significantly).
Disadvantages
Monitoring accuracy is low: query results are correct only at the time instances of periodic updates, but not in between them or at any time of deviation updates.
Location updates are performed regardless of the existence of queries—a high update frequency may improve the monitoring accuracy, but is at the cost of unnecessary updates and query reevaluation.
Server workload using periodic update is not balanced over time: it reaches the peak when updates arrive (they must arrive simultaneously for correct results) and trigger query reevaluation, but is idle for the rest of the time.
Privacy issue is simply ignored by assuming that the clients are always willing to provide their exact positions to the server.
Some recent work attempted to remedy the privacy issue. Location cloaking was proposed to blur the exact client positions into bounding boxes. By assuming a centralized and trustworthy third-party server that stores all exact client positions, various location cloaking algorithms were proposed to build the bounding boxes while achieving the privacy measure such as k-anonymity. However, the use of bounding boxes makes the query results no longer unique. As such, query evaluation in such uncertain space is more complicated. A common approach is to assume that the probability distribution of the exact client location in the bounding box is known and well formed. Therefore, the results are defined as the set of all possible results together with their probabilities. However, all these approaches focused on one-time cloaking or query evaluation; they cannot be applied to monitoring applications where continuous location update is required and efficiency is a critical concern.
Previous work proposed, a monitoring framework where the clients are aware of the spatial queries being monitored, so they send location updates only when the results for some queries might change. Our basic idea is to maintain a rectangular area, called safe region, for each object. The safe region is computed based on the queries in such a way that the current results of all queries remain valid as long as all objects reside inside their respective safe regions. A client updates its location on the server only when the client moves out of its safe region. This significantly improves the monitoring efficiency and accuracy compared to the periodic or deviation update methods. However, this framework fails to address the privacy issue, that is, it only addresses “when” but not “how” the location updates are sent.
Existing System:
• The accuracy is low since the query results are correct only at the time instances of periodic updates, but not in between them or at any time of deviation updates.
• The updates are performed regardless of the existence of Queries a high update frequency may improve the monitoring accuracy, but is at the cost of unnecessary updates and query reevaluation.
• The privacy issue is simply ignored by assuming that the clients are always willing to provide their exact positions to the server.