{"title":"基于压缩感知的分层隐私保护和通信高效压缩","authors":"Hui Huang, Di Xiao, Mengdi Wang","doi":"10.1109/DCC55655.2023.00078","DOIUrl":null,"url":null,"abstract":"Data collection and sharing have a tremendous impact on technology, business and society. Correspondingly, it brings in significant privacy and communication concerns. To this end, we present a hierarchical privacy-preserving and communication-efficient compression scheme via compressed sensing (CS) to address these two issues. In the encoding stage, the obfuscated sensitive regions and non-sensitive regions are compressed and encrypted simultaneously. Consequently, the semi-authorized users and authorized users are considered in the decoding stage. Additionally, the left annihilator matrices provide various kinds of recovery qualities for real-world requirements, which further achieves communication-efficient compression.","PeriodicalId":209029,"journal":{"name":"2023 Data Compression Conference (DCC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hierarchical Privacy-Preserving and Communication-Efficient Compression via Compressed Sensing\",\"authors\":\"Hui Huang, Di Xiao, Mengdi Wang\",\"doi\":\"10.1109/DCC55655.2023.00078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data collection and sharing have a tremendous impact on technology, business and society. Correspondingly, it brings in significant privacy and communication concerns. To this end, we present a hierarchical privacy-preserving and communication-efficient compression scheme via compressed sensing (CS) to address these two issues. In the encoding stage, the obfuscated sensitive regions and non-sensitive regions are compressed and encrypted simultaneously. Consequently, the semi-authorized users and authorized users are considered in the decoding stage. Additionally, the left annihilator matrices provide various kinds of recovery qualities for real-world requirements, which further achieves communication-efficient compression.\",\"PeriodicalId\":209029,\"journal\":{\"name\":\"2023 Data Compression Conference (DCC)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Data Compression Conference (DCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCC55655.2023.00078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Data Compression Conference (DCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCC55655.2023.00078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hierarchical Privacy-Preserving and Communication-Efficient Compression via Compressed Sensing
Data collection and sharing have a tremendous impact on technology, business and society. Correspondingly, it brings in significant privacy and communication concerns. To this end, we present a hierarchical privacy-preserving and communication-efficient compression scheme via compressed sensing (CS) to address these two issues. In the encoding stage, the obfuscated sensitive regions and non-sensitive regions are compressed and encrypted simultaneously. Consequently, the semi-authorized users and authorized users are considered in the decoding stage. Additionally, the left annihilator matrices provide various kinds of recovery qualities for real-world requirements, which further achieves communication-efficient compression.