MARC 主機 00000nam  2200337   4500 
001    AAI3425066 
005    20120910130320.5 
008    120910s2010    ||||||||||||||||| ||eng d 
020    9781124255668 
035    (UMI)AAI3425066 
040    UMI|cUMI 
100 1  Bamba, Bhuvan 
245 10 Scaling location-based services with location privacy 
       constraints: Architecture and algorithms 
300    241 p 
500    Source: Dissertation Abstracts International, Volume: 71-
       10, Section: B, page: 6217 
500    Adviser: Ling Liu 
502    Thesis (Ph.D.)--Georgia Institute of Technology, 2010 
520    Advances in sensing and positioning technology, fueled by 
       wide deployment of wireless networks, have made many 
       devices location-aware. These emerging technologies have 
       enabled a new class of applications, known as Location-
       Based Services (LBS), offering both new business 
       opportunities and a wide array of new quality of life 
       enhancing services. There are two important challenges for
       location-based service provisioning. How do we scale LBSs 
       in the presence of client mobility and location dependent 
       constraints for the multitude of new, upcoming location-
       based applications under a common framework? How do we 
       provide anonymous location-based services with acceptable 
       performance and quantifiable privacy protection in the 
       next generation of mobile networks, systems and 
520    First, we introduce spatial alarms as the basic primitive 
       to represent a class of location-based services that 
       require location-based trigger capability. We develop a 
       generalized framework and a suite of optimization 
       techniques for server-centric scalable processing of 
       spatial alarms. Our architecture and algorithm development
       provide significant performance enhancement in terms of 
       system scalability compared to naive spatial alarm 
       processing techniques, while maintaining high accuracy for
       spatial alarm processing on the server side and reduced 
       communication costs and energy consumption on the client 
       side. Concretely, we develop safe period optimizations for
       alarm processing and introduce spatial alarm grouping 
       techniques to further reduce the unnecessary safe period 
       computation costs. In addition, we introduce a distributed
       alarm processing architecture that advocates the 
       partitioning of the alarm processing load among the server
       and the relevant mobile clients to reduce the server load 
       and minimize the client-to-server communication cost 
       through intelligent distribution and parallelization. We 
       also explore a variety of optimization opportunities such 
       as incorporating non-spatial constraints into the location
       -based information monitoring problem and utilizing 
       efficient indexing methods such as bitmap indexing to 
       further enhance the performance and scalability of spatial
       alarm processing in the presence of mobility hotspots and 
       skewed spatial alarm distributions 
520    Second, we develop the PrivacyGrid framework for privacy-
       enhanced location service provisioning, focusing on 
       providing customizable and personalized location privacy 
       solutions while scaling the mobile systems and services to
       a large number of mobile users and a large number of 
       service requests. The PrivacyGrid approach has three 
       unique characteristics. First, we develop a three-tier 
       architecture for scaling anonymous information delivery in
       a mobile environment while preserving customizable 
       location privacy. Second, we develop a suite of fast, 
       dynamic location cloaking algorithms. It is known that 
       incorporation of privacy protection measures may lead to 
       an inherent conflict between the level of privacy and the 
       quality of services (QoS) provided by the location-based 
       services. Our location cloaking algorithms can scale to 
       higher levels of location anonymity while achieving a good
       balance between location privacy and QoS. Last but not the
       least; we develop two types of location anonymization 
       models under the PrivacyGrid architecture, one provides 
       the random way point mobility model based location 
       cloaking solution, and the other provides a road network-
       based location privacy model powered by both location k-
       anonymity and segment s-anonymity. A set of graph-based 
       location cloaking algorithms are developed, under the 
       MobiCloak approach, to provide desired levels of privacy 
       protection for users traveling on a road network through 
       scalable processing of anonymous location services 
520    This dissertation, to the best of our knowledge, is the 
       first one that presents a systematic approach to the 
       design and development of the spatial alarm processing 
       framework and various optimization techniques. The concept
       of spatial alarms and the scaling techniques developed in 
       this dissertation can serve as building blocks for many 
       existing and emerging location-based and presence based 
       information and computing services and applications. The 
       second unique contribution made in this dissertation is 
       its development of the PrivacyGrid architecture for 
       scaling anonymous location based services under the random
       waypoint mobility model and its extension of the 
       PrivacyGrid architecture through introducing the MobiCloak
       road-network based location cloaking algorithms with 
       reciprocity support for spatially constrained network 
       mobility model. Another unique feature of the PrivacyGrid 
       and MobiCloak development is its ability to protect 
       location privacy of mobile users while maintaining the end
       -to-end QoS for location-based service provisioning in the
       presence of dynamic and personalized privacy constraints. 
       (Abstract shortened by UMI.) 
590    School code: 0078 
650  4 Computer Science 
690    0984 
710 2  Georgia Institute of Technology 
773 0  |tDissertation Abstracts International|g71-10B 
856 40 |u