  Internet of Things devices are expanding rapidly and generating huge amount
of data. There is an increasing need to explore data collected from these
devices. Collaborative learning provides a strategic solution for the Internet
of Things settings but also raises public concern over data privacy. In recent
years, large amount of privacy preserving techniques have been developed based
on secure multi-party computation and differential privacy. A major challenge
of collaborative learning is to balance disclosure risk and data utility while
maintaining high computation efficiency. In this paper, we proposed privacy
preserving logistic regression model using matrix encryption approach. The
secure scheme is resilient to chosen plaintext attack, known plaintext attack,
and collusion attack that could compromise any agencies in the collaborative
learning. Encrypted model estimate is decrypted to provide true model results
with no accuracy degradation. Verification phase is implemented to examine
dishonest behavior among agencies. Experimental evaluations demonstrate fast
convergence rate and high efficiency of proposed scheme.

Cryptography and Security;Machine Learning;Machine Learning;Other Statistics