# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
###### Abstract  
Data publishing techniques have led to breakthroughs in several areas. These tools provide a promising direction. However, when they are applied to private or sensitive data such as patient medical records, the published data may divulge critical patient information. In order to address this issue, we propose a differential private data publishing method (SSKM_DP) based on the SFLA-Kohonen network, which perturbs sensitive attributes based on the maximum information coefficient to achieve a trade-off between security and usability. Additionally, we introduced a single-population frog jump algorithm (SFLA) to optimize the network. Extensive experiments on benchmark datasets have demonstrated that SSKM_DP outperforms state-of-the-art methods for differentially private data publishing techniques significantly. differential privacy; data publishing Kohonen network; SFLA; maximum information coefficient 1223202313823233233233233233233233233233233233233233233233233323  
vacy from a new perspective of privacy preference. However, the dynamic acquisition and release algorithm needs to rely on data distribution and thus faces challenges in the effectiveness and robustness of the algorithm in the face of unknown data distribution. [PERSON] [3] proposed using grid clustering to realize the differential privacy publishing of location-based statistical data to achieve location statistics in the unit of equal size grid, and they designed a bottom-up grid clustering algorithm through the density classification of wavelet transform. However, there are some limitations. The human living environment is mostly based on the distribution of infrastructure, which cannot be well represented by a grid or tree structure and cannot be used to implement an efficient location-based query mode. [PERSON] [4] proposed a data publishing privacy protection method based on local priority anonymity (LPA), which automatically selects anonymous technology for each anonymous algorithm. [PERSON] [5] believes that anonymity technology is vulnerable to various attacks and proposed an adaptive differential privacy protection method for structured data. It protects the privacy of sensitive information through machine learning (ML), which solves the privacy-utility trade-off problem. [PERSON] [6] proposed an efficient differential privacy spatial information network mechanism that is based on personalized sampling; thus, the network can ensure accurate information privacy while sharing statistical information. The k-means clustering algorithm is relatively simple and efficient to process the dataset, but it is sensitive to the initial points, the number of clusters k needs to be chosen empirically has a great impact on the clustering effect, and it is extremely sensitive to noise, and the k-means algorithm causes the loss of clustering accuracy. Although the DBSCAN algorithm can find clusters of any shape, and the clustering results are less affected by noise, its clustering effect needs to be improved on high-dimensional data, and it cannot be applied to high-dimensional data. Accordingly, we propose an improved clustering method based on the Kohonen neural network. There may be complex correlations in the attributes of the data, the correlation between sensitive and non-sensitive attributes can lead to the disclosure of sensitive information, and attackers can infer sensitive information from non-sensitive information. Accordingly, we introduce the maximum information coefficient to measure the relationship between attributes in the data, and according to the correlation between sensitive attributes and non-sensitive attributes, we perturb different degrees of noise to the cluster in which they are located. Based on the above research, this paper proposes a differential privacy data publishing method (SSKM_DP) based on the SFLA-Kohonen network, which allows the published data to obtain a better privacy protection effect and better availability of the published data. The main contributions of this paper are summarized as follows:  
1.