A Novel Network Data Privacy Protection Algorithm in Big Data

With the advancement of power technology and the popularity of the Internet, power data is increasingly becoming an important factor of production. The traditional data privacy protection algorithm has three problems: long execution time, high concealment rate and poor adaptability. In this paper, a network data privacy protection algorithm based on density clustering is proposed to solve these problems. In the power big data environment, a transformation function that satisfies the characteristics of the network privacy data in a given big data environment is randomly generated. Then the transformation function is used to transform the value of the original network privacy data, and the transformed value is randomized as the result of the random answer. After the density clustering analysis of the network nodes according to the characteristics of the network, the system generates clusters of arbitrary shapes that suit the size. We are able to quantify the validity of the private data by quantifying the amount of information loss of the private data of the network and the amount of information loss of the network privacy data structure Finally, the real network node is inserted into the successfully generated cluster, and the privacy protection of the network data information is completed by adding technologies such as edges.