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Privacy –Preserving Public Auditing for Data Security in Cloud Computing

Privacy –Preserving Public Auditing for Data Security in Cloud Computing. B97201006 林楷軒. Outline. Overview of this paper Motivation and Initialization Detailed Mechanism Some Comments Reference. Overview of this paper. Overview of this paper.

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Privacy –Preserving Public Auditing for Data Security in Cloud Computing

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  1. Privacy –Preserving Public Auditing for Data Security in Cloud Computing B97201006 林楷軒

  2. Outline • Overview of this paper • Motivation and Initialization • Detailed Mechanism • Some Comments • Reference

  3. Overview of this paper

  4. Overview of this paper • In one sentence,Ensure your data authentication in cloud? • Properties of cloud storage • Users always have availabe and scalable space →Need not worry about running out of space • Users need not have real physical storage media →Need not spend money on equipments • Data is not near your hand →Data not accessible when network failure→How to make sure the data authentication?

  5. Overview of this paper Some instances threatening your data in cloud • Cloud Storage Provider deletes your data that you seldom access • Cloud Storage Provider hides data loss incidnets • Internal communication error in clusters of computers in Cloud(Amazon 2008,June 20)

  6. Overview of this paper The solution is: A third party checks you data authentication (Self-checking is too tiring)Requirements: • Checks authentication while preserving privacy • [Exclusive]First model able to support scalable and efficient auditing • [Exclusive]Security justified by concrete experiments • [Mice.]No local copy of data, no more burden to users

  7. Motivation and Initialization

  8. Motivation and Initilization Motivation: Check the authentication of data Nonmenclature Explanation(1): • TPA:Third Party Auditor • User:… • CSP:Cloud Storage Provider 鑑識官 鄉民 Amazon

  9. Motivation and Initilization Nonmenclature Explanation(2) • Public key: (封裝) keys for locking a box • Private key: (開箱) keys for unlocking a box • MAC: (檢查碼)message authentication code.Each piece of data has a MAC code, derived from its content 簡單舉例(MD5) • MD5("The quick brown fox jumps over the lazy dog")9e107d9d372bb6826bd81d3542a419d6 • MD5("The quick brown fox jumps over the lazy dog.")e4d909c290d0fb1ca068ffaddf22cbd0

  10. Motivation and Initilization Phase Nonmenclature: • UserKeyGen: generate the keySigGen: gengerate the verification of meta data(MAC) • CSP:Cloud Storage ProviderGenProof: generate proof of data correctness • TPA:Third Party AuditorVerifyProof:Audit proof from CSP(Amazon)

  11. Have a little break...

  12. Motivation and Initilization Example One:Privacy Leaking • 鄉民: • 生成一把鑰匙，丟給鑑識官 • 製造MAC，丟給Amazon • 上傳檔案給Amazon • 鄉民刪除在自己硬碟上的檔案 • 檢查方式 • 鑑識官向Amazon要檔案(檔案外洩啦…) • 鑑識官自行生成MAC，檢查檔案

  13. Motivation and Initilization Example Two:Finitely many checking times • 鄉民: • 生成N把鑰匙，丟給鑑識官 • 製造N種鑰匙的MAC，丟給鑑識官 • 上傳檔案給Amazon • 鄉民刪除在自己硬碟上的檔案 • 檢查方式 • 鑑識官給Amazon鑰匙，並要求回傳對應MAC值 • Amazon回傳對應的MAC值給鑑識官 • 鑑識官生成一次檢查碼，跟Amazon上的MAC做比對

  14. Motivation and Initilization 優缺點分析： Example1鑑識官:擁有鑰匙，所以可以無限次檢查檔案的完整與否Amazon:必須上傳檔案給鑑識官，暴露隱私，也增加工作量 Example2鑑識官保護了使用者隱私因為MAC是有限的，所以可以偽造答案 下一步，我們要分析： 如先兼顧使用者隱私的同時，也讓鑑識官能無限次檢查檔案？

  15. Detailed Mechanism(?) 這份投影片，我採取的策略： 以定性敘述，取代定量分析

  16. Detailed Mechanism(?) Algebra: • Michael Artin • Algebra Essential Parts: Group Theory Link: Here

  17. Detailed Mechanism(?) Cryptography: • OdedGoldreich • Foundations of Cryptography Essential Parts: ??? Link: Here

  18. Detailed Mechanism(?) User Initilization • 鄉民: • 生成解密鑰匙，丟給鑑識官 • 生成公開參數，丟給Amazon • 生成驗證碼丟給Amazon • 鄉民刪除在自己硬碟上的檔案 • 檢查方式 • 鑑識官向Amazon要求檢查部分的檔案 • Amazon利用混合的公開參數，對原始檔案Hash • Amazon回傳Hash值、驗證碼 • 鑑識官由解密鑰匙解密Hash，與驗證碼做比對

  19. Detailed Mechanism(?) 我很難相信你聽得懂 = =

  20. Detailed Mechanism(?) 白話文解釋： • 抽樣檔案 • 驗證碼 (stored in amazon) • Amazon回傳的Hash Code 關鍵在於： • 單獨 兩者的對應關係，沒有人清楚(亂數生成) • 但是整體正確對應關係，只有鑑識官知道(只有他有private key)

  21. Detailed Mechanism(?) 其他保證的性質： • Low Burden on Amazon:Constant large sending block(mathematical analysis…) • Theoretically, if amazon misses 1% data, TPA only needs to audit for 460, 300 samples with probability more than 99%, 95% • Support for Batch AuditingMathematical Analysis

  22. Detailed Mechanism(?) Mathematical Analysis： • Storage Correctness:Amazon can not generate valid response toward TPA without faithfully storing the data • Privacy Perserving Guarantee:TPA can not derive users’dataconent from the information collected during the auditing porcess

  23. Detailed Mechanism(?) Performance Analysis(Real Expriments) • Compared with old method(+Privacy) • Batch Processing

  24. Some Comments

  25. Some Comments 美中不足(雞蛋裡挑骨頭?) • 過於理想化：TPA既不偏坦CSP也不偏袒使用者 • 對於動態資料未清楚說明：(可以套用[8]的結果) • 只能偵測到問題，無法修復 • 99%偵錯率夠嗎？

  26. Reference

  27. Reference • Wikipedia: • Algebra:Michael Artin, 2nd Edition • Foundations of Cryptography:OdedGoldreich • Some slides from 陳君明老師 • Privacy Preserving Public Auditing for Data Storage Security in Cloud Computing(including some reference)

  28. Q & A?

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