Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

Nowadays, Internet of Things (IoT) is widely deployed and brings great opportunities to change people's daily life. To realize more effective human-computer interaction in the IoT applications, the Question Answering (QA) systems implanted in the IoT services are supposed to improve the ability to understand natural language. Therefore, the distributed representation of words, which contains more semantic or syntactic information, has been playing a more and more important role in the QA systems. However, learning high-quality distributed word vectors requires lots of storage and computing resources, hence it cannot be deployed on the resource-constrained IoT devices. It is a good choice to outsource the data and computation to the cloud servers. Nevertheless, it could cause privacy risks to directly upload private data to the untrusted cloud. Therefore, realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task. In this paper, we present a novel efficient word vector learning scheme over encrypted data. We first design a series of arithmetic computation protocols. Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data. The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy. Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.