TY - JOUR
T1 - An efficient and privacy-aware meeting scheduling scheme using common computational space
AU - Huda, Md Nurul
AU - Kamioka, Eiji
AU - Yamada, Shigeki
PY - 2007/3
Y1 - 2007/3
N2 - Privacy is increasingly viewed as a key concern in multi-agent based algorithms for Distributed Constraint Satisfaction Problems (DCSP) such as the Meeting Scheduling (MS) problem. Many algorithms aim for a global objective function and as a result, incur performance penalties in computational complexity and personal privacy. This paper describes a mobile agent-based scheduling scheme called Efficient and Privacy-aware Meeting Scheduling (EPMS), which results in a tradeoff among complexity, privacy, and global utility. It also introduces a privacy loss model for collaborative computation, multiple criteria for evaluating privacy in the MS problem, and a privacy measurement metric called the Min privacy metric. We have utilized a common computational space in EPMS for reducing the complexity and the privacy loss in the MS problem. The analytical results show that EPMS has a polynomial time computational complexity. In addition, simulation results show that the obtained global utility for scheduling multiple meetings with EPMS is close to the optimal level and the resulting privacy loss is less than for those in existing algorithms.
AB - Privacy is increasingly viewed as a key concern in multi-agent based algorithms for Distributed Constraint Satisfaction Problems (DCSP) such as the Meeting Scheduling (MS) problem. Many algorithms aim for a global objective function and as a result, incur performance penalties in computational complexity and personal privacy. This paper describes a mobile agent-based scheduling scheme called Efficient and Privacy-aware Meeting Scheduling (EPMS), which results in a tradeoff among complexity, privacy, and global utility. It also introduces a privacy loss model for collaborative computation, multiple criteria for evaluating privacy in the MS problem, and a privacy measurement metric called the Min privacy metric. We have utilized a common computational space in EPMS for reducing the complexity and the privacy loss in the MS problem. The analytical results show that EPMS has a polynomial time computational complexity. In addition, simulation results show that the obtained global utility for scheduling multiple meetings with EPMS is close to the optimal level and the resulting privacy loss is less than for those in existing algorithms.
KW - Algorithm
KW - Constraint
KW - Distributed computing
KW - Mobile agent
KW - Privacy
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U2 - 10.1093/ietisy/e90-d.3.656
DO - 10.1093/ietisy/e90-d.3.656
M3 - Article
AN - SCOPUS:33947114868
VL - E90-D
SP - 656
EP - 667
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 3
ER -