Privacy Preserving Collaborative Forecasting Based on Dynamic Exponential Smoothing
The development and deployment of private forecasting technologies could allow supply chain collaborations to take place without revealing any participants’ data to the others, reaping the benefits of collaboration while avoiding the drawbacks. Atallah (2004)1,3 is a step towards this goal, as it gives protocols for forecasting that reveal to the participants the desired answers yet do not reveal to any participant any other participants private data. But the smoothing coefficient αused in Atallah (2004)1,3 is assumed to be public and constant, but most of the time series, particularly in the complex economic system, many random observations of the sequence do not have a smoothing coefficient which does not change. Therefore, traditional exponential smoothing model for forecasting has a marked deviation, even serious distortion. So the assumption of constant is out of accordance with the practice. A novel part of this work is that the dynamic smoothing coefficient is established for exponential smoothing, and a corresponding privacy preserving collaborative forecasting algorithm is provided.
XIE Cui-hua ZHONG Wei-jun ZHANG Yu-lin HE Qi-zhi
Southeast University, Nanjing, CO 211102 China
国际会议
2007年IEEE灰色系统与智能服务国际会议(2007 IEEE International Conference on Grey Systems and Intelligent Services)
南京
英文
2007-11-18（万方平台首次上网日期，不代表论文的发表时间）