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Data fusion and privacy protection technologies are both the research focuses in the field of wireless sensor networks. When the sensor network is in a harsh environment, the sensor nodes will face the danger of malicious entity attack in the data fusion progress. The efficiency and privacy protection of sensor network data fusion are very important. The traditional data fusion privacy protection algorithm has the problems of low data fusion efficiency and low privacy protection level. These problems are to be solved in this study. An improved cluster-based privacy data aggregation (I-CPDA) is proposed, which combines data slicing and false interference data technology. The experimental results of the algorithm show that the data fusion accuracy of the I-CPDA algorithm increases faster than the traditional algorithm with the time interval increasing, and the highest value reaches 90.7%. The fusion accuracy of the traditional CPDA algorithm under the same environment is 68.7%. In the actual test, the interception success rate of the I-CPDA algorithm for data attacks reached 90.74%, while the traditional CPDA was only 76.66. In addition, when the number of nodes in the cluster is 15, the data traffic of the I-CPDA is 56, while the data traffic of the traditional CPDA algorithm in the same environment exceeds 200. Compared with the currently widely used traditional algorithms, the I-CPDA algorithm has obvious advantages in terms of fusion effect, privacy, and efficiency and can be put into practical application. [ABSTRACT FROM AUTHOR]

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]