Biblio

Most of the current intelligent Internet of Things (IoT) products take neural network-based speech recognition as the standard human-machine interaction interface. However, the traditional speech recognition frameworks for smart IoT devices always collect and transmit voice information in the form of plaintext, which may cause the disclosure of user privacy. Due to the wide utilization of speech features as biometric authentication, the privacy leakage can cause immeasurable losses to personal property and privacy. Therefore, in this paper, we propose an outsourced privacy-preserving speech recognition framework (OPSR) for smart IoT devices in the long short-term memory (LSTM) neural network and edge computing. In the framework, a series of additive secret sharing-based interactive protocols between two edge servers are designed to achieve lightweight outsourced computation. And based on the protocols, we implement the neural network training process of LSTM for intelligent IoT device voice control. Finally, combined with the universal composability theory and experiment results, we theoretically prove the correctness and security of our framework.

Smart grid systems data has been exposed to several threats and attacks from different perspectives and have resulted in several system failures. Obtaining security of data and key exposure and enhancing system ability in data collection and transmission process are challenging, on the grounds smart grid data is sensitive and enormous sum. In this paper we introduce smart grid data security method along with advanced Cipher text policy attribute based encryption (CP-ABE). Cloud supported IoT is widely used in smart grid systems. Smart IoT devices collect data and perform status management. Data obtained from the IOT devices will be divided into blocks and encrypted data will be stored in different cloud server with different encrypted keys even when one cloud server is assaulted and encrypted key is exposed data cannot be decrypted, thereby the transmission and encryption process are done in correspondingly. We protect access-tree structure information even after the data is shared to user by solving revocation problem in which cloud will inform data owner to revoke and update encryption key after user has downloaded the data, which preserves the data privacy from unauthorized users. The analysis of the system concludes that our proposed system can meet the security requirements in smart grid systems along with cloud-Internet of things.