Abstract: In one or more embodiments, the present invention is directed to a blockchain secured, software-defined network and monitoring system comprising: a multi-controller software-defined network (SDN) network layer; a blockchain based security and autonomy layer; a deep learning-driven decision making layer comprising the one or more computational centers and a horizontal data plane layer. In some embodiments, the present invention is directed to methods for ensuring the integrity of a control commands and optimizing performance and security using the blockchain secured, software-defined network and monitoring system. In various embodiments, the present invention relates to methods for extracting useful features from said labelled and non-labelled data contained in the horizontal data plane layer in the blockchain secured, software-defined network and monitoring system using a knowledge domain-enabled hybrid semi-supervision learning method.

Abstract: In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.

Abstract: In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.