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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/124876
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124876


    Title: 應用 Intel SGX 於多重資料源功能加密:落實機器學習二元分類
    Applying Intel SGX for Multi-Input Functional Encryption on Binary Classification of Machine Learning
    Authors: 林信甫
    Lin, Sin-Fu
    Contributors: 胡毓忠
    Hu, Yuh-Jong
    林信甫
    Lin, Sin-Fu
    Keywords: 隱私保護
    雲端計算安全
    功能加密
    多重資料源功能加密
    安全式機器學習
    Privacy protection
    Secure cloud computing
    Functional encryption
    Multi-input functional encryption
    Intel SGX
    Date: 2019
    Issue Date: 2019-08-07 16:37:01 (UTC+8)
    Abstract: 網際網路和行動裝置高度普及,各式各樣的隱私資料上傳至雲端進行分析運用,然而駭客入侵雲端作業系統、VMM (Virtual Machine Monitor) 或雲端管理員擁有權限查看資料等眾多攻擊面向,皆使得個人隱私資料面臨洩漏風險。本研究使用Intel所提出軟硬體可信執行環境解決方案:SGX (Software Guard Extensions) ,為雲端隱私保護議題提出一個包含使用者、雲端業者、SecaaS(Security as a Service)和MLaaS(Machine Learning as a Service)提供者等四種角色的架構,並設計各個角色間資料、加解密過程與運算流程,以多重資料源功能加密於機器學習的應用,說明此架構滿足資料在儲存、傳遞、使用中皆擁有隱私保護效果。本論文亦闡述SGX限制與安全議題,並進一步與差分隱私、全同態加密進行隱私保護應用之比較。
    Due to the fact that mobile devices and the usage of the internet have become integral parts of our lives, various kinds of private data have been collected and uploaded to the cloud for analysis. Followed by, hackers attack cloud OS, VMM(Virtual Machine Monitor); cloud administrators take on unauthorized action, all leave privacy data at risk. This research aims to resolve the issue by conducting SGX (Software Guard Extensions), Intel’s software and hardware trusted execution environment solution, to propose a software architecture. The designed architecture contains four characters, Users, Cloud Service Provider, Security as a Service and Machine Learning as a Service, which then designed data flow, encryption/decryption flow as well as computation flow between the characters. To explain how the architecture meets the privacy protection demands of data at all time (at-rest, in-transit, and in-use), the research takes Multi-Input Functional Encryption on binary classification of Machine Learning as examples.
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    Description: 碩士
    國立政治大學
    資訊科學系
    1049710011
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1049710011
    Data Type: thesis
    DOI: 10.6814/NCCU201900580
    Appears in Collections:[資訊科學系] 學位論文

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