STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Commodity-based cryptography (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Multi party computations: past and present
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
Protecting data privacy in private information retrieval schemes
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Journal of the ACM (JACM)
Oblivious transfer and polynomial evaluation
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Proceedings of the 1998 workshop on New security paradigms
Selective private function evaluation with applications to private statistics
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Quantum symmetrically-private information retrieval
Information Processing Letters
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
A tight lower bound for restricted PIR protocols
Computational Complexity
Secure multiparty computation of approximations
ACM Transactions on Algorithms (TALG)
Secure function evaluation with ordered binary decision diagrams
Proceedings of the 13th ACM conference on Computer and communications security
A new efficient privacy-preserving scalar product protocol
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Towards Empirical Aspects of Secure Scalar Product
ISA '08 Proceedings of the 2008 International Conference on Information Security and Assurance (isa 2008)
Peer-to-Peer Private Information Retrieval
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
Detecting LTR structures in human genomic sequences using profile hidden Markov models
Expert Systems with Applications: An International Journal
A hidden Markov model-based text classification of medical documents
Journal of Information Science
Occam and Bayes in predicting category intuitiveness
Artificial Intelligence Review
Providing predictions on distributed HMMs with privacy
Artificial Intelligence Review
Secure multi-party computation made simple
Discrete Applied Mathematics - Special issue: Coding and cryptography
Computationally private information retrieval with polylogarithmic communication
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Visual speech recognition using motion features and hidden Markov models
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Accredited symmetrically private information retrieval
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
A linear lower bound on the communication complexity of single-server private information retrieval
TCC'08 Proceedings of the 5th conference on Theory of cryptography
Towards secure bioinformatics services (short paper)
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
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Hidden Markov models (HMMs) are widely used in practice to make predictions. They are becoming increasingly popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal processing, and so on. However, traditional HMMs do not allow people and model owners to generate predictions without disclosing their private information to each other. To address the increasing needs for privacy, this work identifies and studies the private prediction problem; it is demonstrated with the following scenario: Bob has a private HMM, while Alice has a private input; and she wants to use Bob's model to make a prediction based on her input. However, Alice does not want to disclose her private input to Bob, while Bob wants to prevent Alice from deriving information about his model. How can Alice and Bob perform HMMs-based predictions without violating their privacy? We propose privacy-preserving protocols to produce predictions on HMMs without greatly exposing Bob's and Alice's privacy. We then analyze our schemes in terms of accuracy, privacy, and performance. Since they are conflicting goals, due to privacy concerns, it is expected that accuracy or performance might degrade. However, our schemes make it possible for Bob and Alice to produce the same predictions efficiently while preserving their privacy.