Abstract
The popularity of smart home has added a lot of convenience to people’s lives. However, while users use these smart products, users’ privacy data has also been leaked and it may cause some risks. Besides, because of untrusted third-party servers, we simply use traditional privacy-preserving methods could no longer protect users’ private information effectively. In order to solve these problems, this paper proposes a privacy-preserving method for multi-private data: We first determine the privacy data format that needs to be protected, such as audio or text. Secondly, if the data format is text, we will use the local differential privacy method. We first obtain the key attributes of the user from the key information chain, and then select the appropriate localized differential privacy method according to the text characteristics of the key attributes. The user realizes the local disturbance of the data and then uploads it to the data collection center– the cloud platform. Finally, when an attacker attempts to obtain user information from the cloud platform, it uses the central differential privacy method to add noise and the noise-added data is transmitted to the attacker. If the data format is voice frequency, we first convert the voice information into binary code, then chaotically encrypt the binary code, and upload the encrypted binary code to the cloud platform. We verify the effectiveness of our methods by experiments, and it can protect users’ privacy information better.
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Wang, H., Feng, T., Ren, Z., Gao, L., Zheng, J. (2020). Towards Efficient Privacy-Preserving Personal Information in User Daily Life. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_42
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DOI: https://doi.org/10.1007/978-3-030-44751-9_42
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