# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 2 Related Works  
The privacy data release model based on differential privacy protection is mainly divided into two ways:  
1. Noise is directly added to the original data record, and then the data with noise is released. This method has high privacy protection ability, but it leads to the poor utility of published data. 2. First, the original data is processed by using compression, transformation, and other technologies, and then noise is added to the processed data. Finally, the data with noise are released. Although this method may lead to a small part of the data information being missing, it greatly improves the effectiveness of published data. In both methods, the clustering grouping method is used to process the original data, and then the noise is added to each cluster after transformation, which can greatly reduce the noise added to satisfy the differential privacy. At present, there have been some research results from private data publishing methods based on clustering ideas, but these have some problems to some extent. [PERSON] et al. [7] combined \(k\)-anonymity with differential privacy and realized \(k\)-anonymity [8] through micro-clustering, adding noise to each cluster, realizing the differentiation of differential privacy from individual to cluster, reducing the amount of noise absorbed to satisfy differential privacy, and improving the availability of published data. A differential privacy protection method based on \(k\)-means clustering was proposed [9], which uses the clustering center point to replace the privacy in the original records. However, this method is limited by the size of the data, and the availability of clustering results is highly dependent on the size of the privacy budget. [PERSON] et al. [10] carried out micro-clustering according to the level of attributes to improve the homogeneity within the cluster, to reduce information loss, and to improve the availability of data. However, the algorithm has very high computational requirements, which requires high running time and space complexity to process large data. [PERSON] et al. [11] proposed an efficient micro-aggregation method to anonymize multidimensional numerical data by reducing the number of attributes through principal component analysis. The algorithm realized data privacy protection and improved the utility of published multidimensional numerical data, but it was not applicable to discrete attributes and compound attributes. [PERSON] et al. [1] defined three different security levels for different sensitive attribute values proposed an \(l\)-diversity model for multiple sensitive attributes [1; 12], and also proposed three greedy algorithms to achieve \(l\)-diversity for multiple sensitive attributes. This algorithm can solve the problem that information loss increases greatly with an increase in the number of sensitive attributes. [PERSON] et al. [13] proposed a mobile social network privacy protection scheme supporting the K-nearest neighbor search for the first time, which reduces the communication cost between users and servers and reduces the location information and search pattern leaked to servers. Sensitivity calculation methods based on different center cross-distance clustering have been proposed [14] and so have published data satisfying differential privacy protection. However, the method does not delve further into the more flexible micro-aggregation method. [PERSON] et al. [15] studied data publishing based on probabilistic principal component analysis, and [PERSON] et al. [16] studied data publishing based on a neural network multi-cluster distributed algorithm. [PERSON] et al. [17] proposed an anonymization method to protect the privacy of micro data with multiple sensitive properties through anatomy and arrangement. In this paper, the naive multi-sensitive buckets and the nearest multi-sensitive buckets are used to anonymize the data. This approach only works for a single release, rather than focusing on multiple releases. [PERSON] et al.