Providing predictions on distributed HMMs with privacy

Abstract

As forecasting is increasingly becoming important, hidden Markov models (HMMs) are widely used for prediction in many applications such as finance, marketing, bioinformatics, speech recognition, and so on. After creating an HMM, the model owner can start providing predictions. When the model is owned by one party, predictions can be easily provided. However, it becomes a challenge when the model is horizontally or vertically distributed between various parties, even competing companies. The parties want to integrate the split models they own for better forecasting purposes. Due to privacy, financial, and legal reasons; however, they do not want to share their models. We investigate how such parties produce predictions on the distributed model without violating their privacy. We then analyze our proposed schemes in terms of accuracy, privacy, and performance; and finally present our findings.