Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices
Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices
复制标题
智能物联网设备的隐私保护外包语音识别
DOI：
10.1109/jiot.2019.2917933
复制
发表时间：
2019-10-01
影响因子：
10.6
通讯作者：
Li, Feifei
中科院分区：
文献类型：
--
作者：
Ma, Zhuo;Liu, Yang;Li, Feifei
目前的智能物联网产品大多采用基于神经网络的语音识别作为标准的人机交互界面。然而，传统的智能物联网设备语音识别框架总是以明文的形式收集和传输语音信息，这可能会导致用户隐私的泄露。由于语音特征作为生物特征认证的广泛应用，隐私泄露会对个人财产和隐私造成不可估量的损失。因此，在本文中，我们提出了一种用于长短期记忆（LSTM）神经网络和边缘计算的智能物联网设备的外包隐私保护语音识别框架（OPSR）。在该框架中，设计了两个边缘服务器之间一系列基于加性秘密共享的交互协议，以实现轻量级的外包计算。并基于这些协议实现了LSTM的神经网络训练过程，用于智能物联网设备语音控制。最后，结合通用可组合性理论和实验结果，从理论上证明了该框架的正确性和安全性。
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.