{"title":"基于有效隐私保护数据挖掘技术的社交网络安全数据贡献与检索","authors":"Dharmendra Ambani, Kishor H. Atkotiya","doi":"10.1109/CCGE50943.2021.9776454","DOIUrl":null,"url":null,"abstract":"Privacy Preserving Data-Mining is the major use of data- mining methods for ensuring privacy of personal information. Data-mining algorithms search for the information that is most valuable. A major aspect of Privacy Preserving Data Mining is the safeguarding of sensitive information against unauthorized access. The Secure Data Contribution Retrieval algorithm assigns a privacy policy and security is assigned depending on the application needs and compatibility. This method is capable of meeting the requirements for numerous datasets. Currently, social media sites such as Facebook, Twitter, and YouTube are quite popular. Then, the expanded attribute-based encryption methodology allows users to transfer data contents across orbit software networks. Data leakage occurs during the gathering and storage of user Orbit Software Networks in an insecure distributed or centralized system. Third, the suggested Level by Level Security Optimization and Content Visualization algorithm helps prevent privacy problems when sharing information and visualizing data. They use privacy levels at the individual level following the assessment of the privacy compatibility of orbit software networks application. Experimental analysis employs the data from social datasets.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy Preserving Data Mining Techniques\",\"authors\":\"Dharmendra Ambani, Kishor H. Atkotiya\",\"doi\":\"10.1109/CCGE50943.2021.9776454\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Privacy Preserving Data-Mining is the major use of data- mining methods for ensuring privacy of personal information. Data-mining algorithms search for the information that is most valuable. A major aspect of Privacy Preserving Data Mining is the safeguarding of sensitive information against unauthorized access. The Secure Data Contribution Retrieval algorithm assigns a privacy policy and security is assigned depending on the application needs and compatibility. This method is capable of meeting the requirements for numerous datasets. Currently, social media sites such as Facebook, Twitter, and YouTube are quite popular. Then, the expanded attribute-based encryption methodology allows users to transfer data contents across orbit software networks. Data leakage occurs during the gathering and storage of user Orbit Software Networks in an insecure distributed or centralized system. Third, the suggested Level by Level Security Optimization and Content Visualization algorithm helps prevent privacy problems when sharing information and visualizing data. They use privacy levels at the individual level following the assessment of the privacy compatibility of orbit software networks application. Experimental analysis employs the data from social datasets.\",\"PeriodicalId\":130452,\"journal\":{\"name\":\"2021 International Conference on Computing, Communication and Green Engineering (CCGE)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computing, Communication and Green Engineering (CCGE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCGE50943.2021.9776454\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGE50943.2021.9776454","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy Preserving Data Mining Techniques
Privacy Preserving Data-Mining is the major use of data- mining methods for ensuring privacy of personal information. Data-mining algorithms search for the information that is most valuable. A major aspect of Privacy Preserving Data Mining is the safeguarding of sensitive information against unauthorized access. The Secure Data Contribution Retrieval algorithm assigns a privacy policy and security is assigned depending on the application needs and compatibility. This method is capable of meeting the requirements for numerous datasets. Currently, social media sites such as Facebook, Twitter, and YouTube are quite popular. Then, the expanded attribute-based encryption methodology allows users to transfer data contents across orbit software networks. Data leakage occurs during the gathering and storage of user Orbit Software Networks in an insecure distributed or centralized system. Third, the suggested Level by Level Security Optimization and Content Visualization algorithm helps prevent privacy problems when sharing information and visualizing data. They use privacy levels at the individual level following the assessment of the privacy compatibility of orbit software networks application. Experimental analysis employs the data from social datasets.