{"title":"移动云数据计算的选择性加密和面向组件的重复数据删除","authors":"Sejun Song, Baek-Young Choi, Daehee Kim","doi":"10.1109/ICCNC.2016.7440636","DOIUrl":null,"url":null,"abstract":"As smart devices gain their popularity and usage applications become versatile, the users are also hoping to perform resource intensive tasks at anywhere and anytime as conveniently as using their static computers. To overcome the smart device's intrinsic resource limitations in processing, storage, and power, emerging collaborative mobile cloud technologies such as Mobile Cloud Computing (MCC), Mobile-Edge Computing (MEC), and Fog Computing (FC) augment the smart device's capabilities by leveraging distributed and remote cloud resources. However, in collaborative computing environments, the demand for big data processing and exchanges among smart devices is considered as a significant challenge. An effective technique to reduce data at a source device is essential to save network bandwidth and storage spaces. It, in turn, improves the data processing overhead as well as reduces the security vulnerability caused by data movement among the smart devices. In this paper, we design and develop a novel Selective Encryption and Component-Oriented Deduplication (SEACOD) application that achieves both fast and effective data encryption and reduction for MCC services. Specifically, SEACOD efficiently deduplicates redundant objects in files, emails, as well as images exploiting object-level components based on their structures. It also effectively reduces the overall encryption overhead on the mobile devices by adaptively applying compression and encryption methods according to the decomposed data types. Our evaluation using real datasets of structured files shows that the proposed scheme accomplishes as good of storage savings as a variable-block deduplication, while being as fast as a file-level or a large fixed-size block-level deduplication.","PeriodicalId":308458,"journal":{"name":"2016 International Conference on Computing, Networking and Communications (ICNC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Selective encryption and component-oriented deduplication for mobile cloud data computing\",\"authors\":\"Sejun Song, Baek-Young Choi, Daehee Kim\",\"doi\":\"10.1109/ICCNC.2016.7440636\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As smart devices gain their popularity and usage applications become versatile, the users are also hoping to perform resource intensive tasks at anywhere and anytime as conveniently as using their static computers. To overcome the smart device's intrinsic resource limitations in processing, storage, and power, emerging collaborative mobile cloud technologies such as Mobile Cloud Computing (MCC), Mobile-Edge Computing (MEC), and Fog Computing (FC) augment the smart device's capabilities by leveraging distributed and remote cloud resources. However, in collaborative computing environments, the demand for big data processing and exchanges among smart devices is considered as a significant challenge. An effective technique to reduce data at a source device is essential to save network bandwidth and storage spaces. It, in turn, improves the data processing overhead as well as reduces the security vulnerability caused by data movement among the smart devices. In this paper, we design and develop a novel Selective Encryption and Component-Oriented Deduplication (SEACOD) application that achieves both fast and effective data encryption and reduction for MCC services. Specifically, SEACOD efficiently deduplicates redundant objects in files, emails, as well as images exploiting object-level components based on their structures. It also effectively reduces the overall encryption overhead on the mobile devices by adaptively applying compression and encryption methods according to the decomposed data types. Our evaluation using real datasets of structured files shows that the proposed scheme accomplishes as good of storage savings as a variable-block deduplication, while being as fast as a file-level or a large fixed-size block-level deduplication.\",\"PeriodicalId\":308458,\"journal\":{\"name\":\"2016 International Conference on Computing, Networking and Communications (ICNC)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Computing, Networking and Communications (ICNC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCNC.2016.7440636\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Computing, Networking and Communications (ICNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCNC.2016.7440636","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Selective encryption and component-oriented deduplication for mobile cloud data computing
As smart devices gain their popularity and usage applications become versatile, the users are also hoping to perform resource intensive tasks at anywhere and anytime as conveniently as using their static computers. To overcome the smart device's intrinsic resource limitations in processing, storage, and power, emerging collaborative mobile cloud technologies such as Mobile Cloud Computing (MCC), Mobile-Edge Computing (MEC), and Fog Computing (FC) augment the smart device's capabilities by leveraging distributed and remote cloud resources. However, in collaborative computing environments, the demand for big data processing and exchanges among smart devices is considered as a significant challenge. An effective technique to reduce data at a source device is essential to save network bandwidth and storage spaces. It, in turn, improves the data processing overhead as well as reduces the security vulnerability caused by data movement among the smart devices. In this paper, we design and develop a novel Selective Encryption and Component-Oriented Deduplication (SEACOD) application that achieves both fast and effective data encryption and reduction for MCC services. Specifically, SEACOD efficiently deduplicates redundant objects in files, emails, as well as images exploiting object-level components based on their structures. It also effectively reduces the overall encryption overhead on the mobile devices by adaptively applying compression and encryption methods according to the decomposed data types. Our evaluation using real datasets of structured files shows that the proposed scheme accomplishes as good of storage savings as a variable-block deduplication, while being as fast as a file-level or a large fixed-size block-level deduplication.