Abstract
Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two‐party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.
Highlights
The development of emerging computing technologies have brought opportunity for various industries, such as hyperspectral remote sensing image algorithms [1, 2], classification algorithms [3], matrix operations under linear systems [4, 5], and data generated by Internet of Things (IoT) devices
Deep learning technology has been widely used in IoT applications [6], e.g., smart home [7], smart city [8, 9], and autonomous driving [10]
In the scenario of applying deep learning technology to big data in the IoT, in order to train a neural network, large amounts of data need to be obtained from the IoT devices
Summary
The development of emerging computing technologies (e.g., cloud computing) have brought opportunity for various industries, such as hyperspectral remote sensing image algorithms [1, 2], classification algorithms [3], matrix operations under linear systems [4, 5], and data generated by Internet of Things (IoT) devices. If a resigned employee sells IoT big data in exchange for economic benefits, it will endanger the interests of the company and harm people’s personal safety This shows that it is necessary to set the validity period for each user attribute. We consider the data privacy problems of big data generated in the field of IoT for mobile computing and use attribute revocation idea [29, 30], propose an IoT big data privacy protection scheme based on time and the number of decryption restrictions. This scheme combines homomorphic encryption and attribute-based encryption.
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