Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 商學組
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68171
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor游張松
dc.contributor.authorTed Tsei Kuoen
dc.contributor.author郭志義zh_TW
dc.date.accessioned2021-06-17T02:13:59Z-
dc.date.available2018-01-04
dc.date.copyright2018-01-04
dc.date.issued2017
dc.date.submitted2017-11-20
dc.identifier.citation[1] Ragunathan Rajkumar. A Cyber-Physical Future. In Proceedings of the IEEE, Volume: 100, Issue: Special Centennial Issue, Page 1309 – 1312, 2012
[2] C.-S. Yu. A Novel VCC and Business Model for Designer Entrepreneurs. Global Business & International Management Conference, August 2012, Portland, OR, USA
[3] Austin Carr. 7 Creepy Faux Pas of Google CEO Eric Schmidt. In Fast Company, October 6, 2010.
[4] John Cheney-Lippold. We Are Data: Algorithms and the Making of Our Digital Selves. New York University Press, 2017
[5] Ramnath K. Chellappa and Raymond G. Sin. Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma. In Information Technology and Management. 6 (2), 181-202, 2005
[6] IEEE Systems Journals Special Issue on Intelligent Internet of Things. IEEE Systems Journal, Volume: 10, Issue: 3, 1107 – 1110, 2016
[7] T. C. Sottek and Janus Kopfstein. Everything You Need to Know About PRISM. In The Verge, July 17, 2013
[8] Josh Chin and Gillian Wong. China’s New Tool for Social Control: A Credit Rating for Everything. In The Wall Street Journal, Nov 28, 2016
[9] Priyan Jain, Manasi Gyanchandani, and Nilay Khare. Big Data Privacy: A Technological Perspective and Review. Journal of Big Data, 2016
[10] Abid Mehmood, Iynkaran Natgunanathan, Yong Xiang, Guang Hua, and Song Guo. Protection of Big Data Privay. Special Section on Theoretical Foundations For Big Data Applications: Challenges and Opportunities, April 27, 2016
[11] Yair Silbermintz. Socketpuppet. https://github.com/MisterGlass/SocketPuppet
[12] Daniel C. Howe and Helen Nissenbaum. TrackMeNot. https://cs.nyu.edu/trackmenot/
[13] MaskMe. https://www.abine.com/maskme/faq/#whatisit
[14] Lifei Wei, Haojin Zhu, Zhenfu Cao, Weiwei Jia, Yunlu Chen, Athanasios V. Vasilakos. Security and Privacy for Storage and Computation in Cloud Computing. Information Science, 258, pp. 371-386, 2014
[15] Cong Wang, Qian Wang, Kui Ren, and Wenjing Lou. Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing. In Proceedings of IEEE International Conference on INFOCOM, 2010, pp 1-9
[16] Chang Liu, Jinjun Chen, Laurence T. Yang, Xuyun Zhang, Chi Yang, Rajiv Ranjan, and Ramamohanarao Kotagiri. Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient verifiable Fine-Grained Updates. In IEEE Trans. on Parallel and Distributed Systems, vol. 25, no. 9, 2014, p2234 – 2244
[17] Zhifeng Xiao and Yang Xiao. Security and Privacy in Cloud Computing. In IEEE Trans. on Communications Surveys and Tutorials, vol. 15, no. 2, 2013, p 843-59.
[18] Kaihe Xu, Hao Yue, Linke Guo, Yuanxiong Guo, Yuguang Guo, and Yuguang Fang. Privacy-Preserving Machine Learning Algorithms for Big Data Systems. In Distributed Computing Systems (ICDCS) IEEE 35th International Conference, 2015
[19] Yunquan Zhang, Ting Cao, Shigang Li, Xinhui Tian, Liang Yuan, Haipeng Jia, and Athanasios V. Vasilakos. Parallel Processing Systems for Big Data: A Survey. in Proceedings of the IEEE, vol: 104, issue: 11, 2016
[20] European Committee. General Data Protection Regulation. In Official Journal of the European Union. Regulation (EU) 2016/679, April 7, 2016
[21] Latanya Sweeney. k-anonymity: A model for protecting privacy. In International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 10 (05): 557-570, 2002
[22] Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam. l-diversity: Privacy Beyond k-anonymity. In ACM Transact. on Knowledge Discovery from Data (TKDD), 1(1):3, 2007
[23] Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. t-closeness: Privacy Beyond k-anonymity and l-diversity. In ICDE, vol. 7, pages 106-115, 2007
[24] Cynthia Dwork. Differential Privacy. In Automata, Languages, and Programming, pages 1-12, Springer, 2006
[25] Craig Gentry. Fully homomorphic encryption using ideal lattices. In STOC, volume 9, pages 169-178, 2009
[26] Adam Meyerson and Ryan Williams, On the Complexity of Optimal k-anonymity. In Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART symposium on Principles of Database Systems. New York, NY; ACM:223-8.
[27] C. Liu, R. Ranjan, X. Zhang, C. Yang, D. Georgakopoulos, and J. Chen. Public Auditing of Dynamic Big Data Storage in Cloud Computing – A Survey. In: Proceedings of IEEE International Conference on Computational Science and Engineering. 2013, p. 1128-35.
[28] Bart Willemsen. Hype Cycle for Privacy, 2017. Gartner, July 2017
[29] Satoshi Nakamoto. Bitcoin: A Peer-to-Peer Electonic Cash System. https://bitcoin.org/bitcoin.pdf
[30] Jonathan Penney. Chilling Effects: Online Surveillance and Wikipedia Use. In Berkeley Technology Law Journal, Vol. 31, No. 1, p. 117-83, 2016
[31] Jerry Gao, H.-S. J. Tsao, and Ye Wu. Testing and Quality Assurance for Component-based Software. Artech House. pp 170-.
[32] Guy Zyskind, Oz Nathan, and Alex “Sandy” Pentland. Decentralizing Privacy: Using Blockchain to Protect Personal Data. In IEEE CS Security and Privacy Workshops, 2015
[33] Alessandro Vinciarelli and Alex “Sandy” Penland. New Social Signals in a New Interaction World: The Next Frontier for Social Signal Processing. In IEEE Systems, Man, & Cybernetics Magazine, April 2015
[34] Parity, https://parity.io/
[35] Ripple, https://interledger.org/interledger.pdf
[36] Florian Tschorsch and Bjorn Scheuermann. Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currenies. In IEEE Communications Survey & Tutorials, Vol. 18, No. 3, Third Quarter 2016
[37] Ittay Eyal and Emin Gun Sirer. Majority is not enough: Bitcoin mining is vulnerable. In Financial Cryptography and Data Security, page 436-54. Springer, 2014
[38] Nicola Atzei, Massimo Bartoletti, and Tiziana Cimoli. A Survey of Attacks on Ethereum Smart Contracts (SoK). In Proceedings of the 6th International Conference on Principles of Security and Trust – Volume 10204. p. 164-186, April, 2017
[39] Arvind Narayanan, Joseph Bonneau, Edward Felton, Andrew Miller, and Steven Goldfeder. Bitcoin and Cryptocurrency Technologies. Princeton University Press, 2016
[40] Andreas M. Antonopoulos, Mastering Bitcoin: Programming the Open Blockchain, 2nd Ed. Oreilly, 2017
[41] Rudolph C. Merkel. A Digital Signature Based on a Conventional Encryption Function. In Advances in Cryptology – CRYPTO ’87. Lecture Notes in Computer Science. 293. p. 369. 1988
[42] IPFS White Paper, https://github.com/ipfs/ipfs/blob/master/papers/ipfs-cap2pfs/ipfs-p2p-file-system.pdf?raw=true
[43] Ethereum White Paper, https://github.com/ethereum/wiki/wiki/White-Paper
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68171-
dc.description.abstract隱私權是基本人權的一部分。隨著資訊採集或監控技術的進步與普及,我們往往為了獲得「個人化」服務而將保護我們隱私權的工作交給這些資料採集的組織,期待他們會謹守分寸,進到保護我們的隱私之責。 當我們連接到網路時,我們隨時遺留數位痕跡,而這些數位痕跡在經過神秘的演算法處理之後將會決定我們在數位及現實生活的命運。然而對於這些能夠左右我們生活的演算法我們卻無從得知任何細節,因為他們往往被隱藏在國家安全或商業機密之後。在這些演算法所提供的個人化價值服務的背後,他們對個人所造成的衝擊及潛在傷害卻是真實而有持續性的。此乃文獻中所謂的「隱私與個人化的悖論」。
本研究試著去回答從使用者角度如何去平衡隱私與個人化的需求進而防止陷入引起負價值創造循環。雖然這是一個廣泛的題目,從文獻的學習中觀察到這個主題具有四個主要的面向: 身分代表的所有權、資料擁有權、資料安全、法規的遵循。我們從分析「資料生命周期」進而闡述這四個面向可以簡潔地用四個基本因子: 讓使用者擁有控制權、清楚告知程序、贏得信任、擔負法規責任。我們進一步以此發展一個對系統檢視保護隱私權措施的框架,我們稱之為CAT-on-A-stool.
如同我們之前所言,大部分組織的系統運作是不透明的。在這樣的限制之下,為了推進我們的研究,我們籍由從軟體測試領域的「黑盒測試理論」論述只要我們確保在這個系統的輸入端子系統符合CAT-on-A-stool框架,整個系統應該能平衡隱私權與個人化的需求。而這個輸入端子系統即所謂的Identity Access Management (IAM)系統。
在審視完目前的現有的IAM系統及其缺失後,我們提出一個新的基於分散式自主執行單位(decentralized autonomous organization, DAO)的IAM系統,我們稱之為DAO-IAM。此系統賦與使用者擁有數位身分(ID)控制權,並藉由數位身分的控制進而掌控隱私權與個人化的需求的平衡。在DAO-IAM裡,我們並設置一個由不同單位代表人所組成的管理委員會及結合在DAO-IAM裡的智能合約( smart contracts )進行事項表決以確保決策中立性,並藉由DAO的「執行不可改變性」付諸實現。
我們論述DAO-IAM系統符合CAT-on-A-stool。但是,正如所有新的系統所面臨的問題: 被採納性、被接受速度。為此我們亦闡述如何與現有常見服務提供商,如Google,Facebook,Yahoo!等,IAM機制藉由oAuth協議共存。
zh_TW
dc.description.abstractPrivacy is a basic human right. With the advancement and prevailing of data collecting and processing, a.k.a. surveilling, technologies in the data economy era, we often put our privacy at the mercy of the collecting agents, e.g., governments, and big corporations, in exchange for their personalized services. Whenever on the grid, we leave trails of digital breadcrumbs to these agents, whose mystical algorithms further decide our fates in both digital AND physical worlds. It's almost impossible to examine, correct, or even regulate these algorithms since most of them are hidden under the name of national security or trade secrets. And yet, the impact and potential damage behind the perceived values are real to individuals and they could be so profound and long-lasting. In a way, we are trapped in the so-called personalization-privacy paradox [5].
We set out to answer the question: from a user’s perspective, how to re/balance privacy-personalization to avoid the paradox as a mean to prevent the forming of negativity creation cycles (NCC’s) [2]. Although this is a very broad challenge, we have categorized related issues into four aspects: ID ownership, data ownership, data security, and regulation compliance. We further elaborated and concluded, by analyzing a typical data life-cycle, that these four aspects can be succinctly addressed by the four essential factors: control, awareness, trust, and accountability. We, then, used these four factors to develop a privacy-preserving system evaluation framework named CAT-on-A-stool to help us evaluate if a system preserves privacy while allowing users to enjoy personalized services.
As we pointed out that most operations of these organizations are not transparent. To further our analysis, we borrowed a common practice in the software testing field, black box testing. That is, from user’s perspective, the overall system dynamic can be probe through the control of input side without the insights of the black box. We believe if the input of the system, i.e., identity and access management (IAM) system, complied with the CAT-on-A-stool framework, the overall system should balance the privacy protection and the personalization needs.
We examine two common IAM and a newly proposed blockchain based systems with the CAT-on-A-stool framework and found each has their shortcomings. From these study, we propose a novel Decentralized Autonomous Organization (DAO) based IAM solution. The proposed DAO-IAM system is a user-centric global ID system that users have more control over. It consists of a human governance committee to judge and manage policies and audit-related issues, and a DAO to autonomously carry out policies without bias. The DAO-IAM system meets the CAT-on-A-stool evaluation but, like all the new systems, its adoption rate and speed will decide its success. To facilitate the adoption, we have addressed the backward compatibility issue by showing how it works with oAuth systems like Google, Facebook, Yahoo, etc.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T02:13:59Z (GMT). No. of bitstreams: 1
ntu-106-P02748029-1.pdf: 4813096 bytes, checksum: 3731792ccc86cb1b3cc20ea1dbf7e17f (MD5)
Previous issue date: 2017
en
dc.description.tableofcontentsTable of Contents
ACKNOWLEDGEMENTS III
中文摘要 V
THESIS ABSTRACT VII
TABLE OF CONTENTS IX
LIST OF FIGURES X
LIST OF TABLES XII
CHAPTER 1. INTRODUCTION 1
CHAPTER 2. DATA PRIVACY PRESERVATION TECHNIQUES AND REGULATION 7
2.1 DATA PRIVACY-PRESERVING TECHNIQUES REVIEW 7
2.2 REGULATIONS FOR DATA PRIVACY PROTECTION 12
2.3 PRIVACY-PRESERVING TECHNIQUE EVALUATION FRAMEWORK, CAT-ON-A-STOOL 14
2.4 BLACK BOX ANALYSIS 17
2.5 SUMMARY AND THE NEXT STEP 18
CHAPTER 3. REQUIREMENTS AND PRIOR RESEARCH AND PRACTICE IN PRIVACY-PRESERVING IAM SYSTEMS 20
3.1 PRIVACY DILEMMA OF EXISTING IAM SYSTEMS 20
3.2 MAPPING IAM REQUIREMENTS OVER THE CAT-ON-A-STOOL 22
3.3 SUMMARY OF MODELS COMPARISON 28
CHAPTER 4. DAO-IAM: A USER-CENTRIC DAO-BASED IAM SYSTEM 29
4.1 SYSTEM DESIGN CONSIDERATION 29
4.2 TECHNOLOGY CONSIDERATION 32
4.3 OPERATIONS CONSIDERATION - GOVERNANCE COMMITTEE 37
4.4 OVERVIEW OF THE DAO-IAM SYSTEM 38
4.5 KEY COMPONENTS OF THE DAO-IAM 42
4.6 CAT-ON-A-STOOL ANALYSIS OF THE DAO-IAM SYSTEM, M3 52
4.7 DISCUSSION OF THE DAO-IAM 54
CHAPTER 5 CONCLUDING REMARKS 57
5.1 OUR CONTRIBUTIONS 59
5.2 FUTURE WORK 59
REFERENCES 61

List of Figures
FIGURE 1. THE CYBER-PHYSICAL WORLD, CPW 1
FIGURE 2. VCC'S OF A CPW: THE GOOD 2
FIGURE 3. VCC’S OF A CPW: THE BAD 3
FIGURE 4. VCC’S OF A CPW: THE UGLY 5
FIGURE 5. THREE KEY STAGES OF DATA LIFECYCLE 8
FIGURE 6. THE FOUR ASPECTS OF PRIVACY-PRESERVING TECHNIQUES 14
FIGURE 7. GARTNER'S HYPE CYCLE FOR PRIVACY, JULY 2017 15
FIGURE 8. A QUALITATIVE USER-CENTRIC CAT-ON-A-STOOL PRIVACY-PRESERVING EVALUATION FRAMEWORK 16
FIGURE 9. USERS' PERCEIVED VALUE RECEIVED IS MUCH HIGHER THAN THEIR PRIVACY 16
FIGURE 10. CAT-ON-A-STOOL TO REBALANCE DIGITAL PRIVACY AND PERCEIVED VALUES 17
FIGURE 11. CAT-ON-A-STOOL MAPPED ONTO DATA LIFECYCLE 17
FIGURE 12. BLACK BOX TESTING APPROACH 18
FIGURE 13. UNTRUSTED BLACK BOX OPERATIONS OF SERVICE PROVIDERS 19
FIGURE 14. CAT-ON-A-STOOL OF IAM REQUIREMENTS 22
FIGURE 15. M0 MODEL – SILO AND PROPRIETARY 22
FIGURE 16. RESULTS OF M0 CAT-ON-A-STOOL ANALYSIS 24
FIGURE 17. M1 MODEL - FEDERATED ID MODEL 24
FIGURE 18. RESULTS OF M1 CAT-ON-A-STOOL ANALYSIS 26
FIGURE 19. M2 MODEL – BLOCKCHAIN BASED FULLY P2P MODEL 26
FIGURE 20. RESULTS OF M2 CAT-ON-A-STOOL ANALYSIS 27
FIGURE 21. EXAMPLES OF BITCOIN TRANSACTIONS 33
FIGURE 22. ILLUSTRATION OF PROOF OF FUND 34
FIGURE 23. BITCOIN NETWORK AND BLOCKCHAIN 35
FIGURE 24. CONCEPTUAL MACHINE 38
FIGURE 25. ETHEREUM SMART CONTRACTS 39
FIGURE 26. THE DAO-IAM SYSTEM 39
FIGURE 27. ID’S AND ID CLASSES 42
FIGURE 28. ECDSA – PUBLIC KEY CALCULATION GIVEN G AND K 44
FIGURE 29. BITCOIN KEY PAIR EXAMPLE 44
FIGURE 30. BITCOIN ADDRESS 45
FIGURE 31. BASE58 PROCESS 45
FIGURE 32. ADDRESS REGISTRATION 46
FIGURE 33. ID REGISTRATION2 47
FIGURE 34. ID DEREGISTRATION 48
FIGURE 35. ID DEREGISTRATION2 48
FIGURE 36. AUTHENTICATION, OR LOGIN – NATIVE SERVICES SUPPORT 49
FIGURE 37. AUTHENTICATION, OR LOGIN – COMPATIBLE SERVICES 50
FIGURE 38. DAO-IAM INQUIRIES 52
FIGURE 39. DAO-IAM CAT-ON-A-STOOL ANALYSIS 53












List of Tables
TABLE 1. A NON-ANONYMIZED PATIENT DATABASE 10
TABLE 2. RESULTS AFTER APPLYING SUPPRESSION ON 'NAME' AND 'RELIGION' FIELDS 10
TABLE 3. APPLIED GENERALIZATION 10
TABLE 4. SUMMARY OF M0, M1, AND M2 CAT-ON-A-STOOL ANALYSIS 28
TABLE 5.SUMMARY OF M0, M1, M2, AND M3 CAT-ON-A-STOOL ANALYSIS 54
dc.language.isoen
dc.subject分散式自主執行單位zh_TW
dc.subject智能合約zh_TW
dc.subject區塊鏈zh_TW
dc.subject黑盒測試理論zh_TW
dc.subject隱私與個人化的悖論zh_TW
dc.subject身分認證及使用管理系統zh_TW
dc.subject隱私權zh_TW
dc.subjectdecentralization autonomous organization (DAO)en
dc.subjectsmart contracten
dc.subjectblockchainen
dc.subjectblack box testingen
dc.subjectprivacy-personalization paradoxen
dc.subjectidentity and access management (IAM)en
dc.subjectprivacyen
dc.title以區塊鏈相互監督機制建立保護隱私的數位身分証發行、認証管理系統zh_TW
dc.titleDAO-IAM: A User-Centric DAO-Based
Privacy-Preserving IAM System
en
dc.typeThesis
dc.date.schoolyear106-1
dc.description.degree碩士
dc.contributor.oralexamcommittee廖則竣,張舜德,林永松
dc.subject.keyword隱私權,身分認證及使用管理系統,隱私與個人化的悖論,黑盒測試理論,區塊鏈,智能合約,分散式自主執行單位,zh_TW
dc.subject.keywordprivacy,identity and access management (IAM),privacy-personalization paradox,black box testing,blockchain,smart contract,decentralization autonomous organization (DAO),en
dc.relation.page64
dc.identifier.doi10.6342/NTU201704393
dc.rights.note有償授權
dc.date.accepted2017-11-21
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept商學組zh_TW
顯示於系所單位:商學組

文件中的檔案:
檔案 大小格式 
ntu-106-1.pdf
  未授權公開取用
4.7 MBAdobe PDF
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved