Finding the State Sequence Maximizing P(O, I vertical bar lambda) on Distributed HMMs with Privacy

Renckes S., Polat H., Oysal Y.

IEEE Symposium on Computational Intelligence in Cyber Security, Tennessee, United States Of America, 30 March - 02 April 2009, pp.152-158 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Tennessee
  • Country: United States Of America
  • Page Numbers: pp.152-158
  • Anadolu University Affiliated: Yes


Hidden Markov models (HMMs) are widely used by many applications for forecasting purposes. They are increasingly becoming popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal processing, and so on. Given an HMM, an application of HMMs is to choose a state sequence so that the joint, probability of an observation sequence and a state sequence given the model is maximized. Although this seems an easy task if the model is given, it becomes a challenge when the model is distributed between various parties. Due to privacy,,financial, and legal reasons, the model owners might not want to integrate their split models.