TY - JOUR
T1 - PAM
T2 - An efficient and privacy-aware monitoring framework for continuously moving objects
AU - Hu, Haibo
AU - Xu, Jianliang
AU - Lee, Dik Lun
N1 - Funding Information:
This work was supported by the Research Grants Council, Hong Kong SAR, China under Project No. HKBU211206, HKBU211307, HKBU210808, HKBU1/05C, HKBU/FRG08-09/II-48, RGC GRF 615806, and CA05/06.EG03.
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
KW - Location-dependent and sensitive
KW - Mobile applications
KW - Spatial databases
UR - http://www.scopus.com/inward/record.url?scp=76749163866&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2009.86
DO - 10.1109/TKDE.2009.86
M3 - Journal article
AN - SCOPUS:76749163866
SN - 1041-4347
VL - 22
SP - 404
EP - 419
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 3
M1 - 4840343
ER -