A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
'I didn't buy it for myself' privacy and ecommerce personalization
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Privacy-preserving data integration and sharing
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Top-N Recommendation on Horizontally Partitioned Data
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Privacy-preserving collaborative filtering on vertically partitioned data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Private predictions on hidden Markov models
Artificial Intelligence Review
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
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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.