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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.author | Ted Tsei Kuo | en |
| dc.contributor.author | 郭志義 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:13:59Z | - |
| dc.date.available | 2018-01-04 | |
| dc.date.copyright | 2018-01-04 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-11-20 | |
| 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.uri | http://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.abstract | Privacy 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.provenance | Made 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.tableofcontents | Table 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.iso | en | |
| 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.subject | decentralization autonomous organization (DAO) | en |
| dc.subject | smart contract | en |
| dc.subject | blockchain | en |
| dc.subject | black box testing | en |
| dc.subject | privacy-personalization paradox | en |
| dc.subject | identity and access management (IAM) | en |
| dc.subject | privacy | en |
| 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.title | DAO-IAM: A User-Centric DAO-Based
Privacy-Preserving IAM System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| 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.keyword | privacy,identity and access management (IAM),privacy-personalization paradox,black box testing,blockchain,smart contract,decentralization autonomous organization (DAO), | en |
| dc.relation.page | 64 | |
| dc.identifier.doi | 10.6342/NTU201704393 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-11-21 | |
| 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 | |
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