Biblio

Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.

Advances in sensing and tracking technology lead to the proliferation of location-based services. Location service providers (LSPs) often resort to commercial public clouds to store the tremendous geospatial data and process location-based queries from data users. To protect the privacy of LSP's geospatial data and data user's query location against the untrusted cloud, they are required to be encrypted before sending to the cloud. Nevertheless, it is not easy to design a fast and secure location-based query processing scheme over the encrypted data. In this paper, we propose a Fast and Secure kNN (FSkNN) scheme to support secure k nearest neighbor (k NN) search in cloud computing. We reveal the inherent connection between an Sk NN protocol and a secure range query protocol and further describe how to construct FSkNN based on a secure range query protocol. FSkNN leverages a customized accuracy-assured strategy to ensure the result accuracy and adopts a data structure named random Bloom filter (RBF) to build a secure index for efficiently searching. We formally prove the security of FSkNN under the random oracle model. Our evaluation results show that FSkNN is highly practical.