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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
| dc.contributor.author | Wei-Ting Hsieh | en |
| dc.contributor.author | 謝威廷 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:58:01Z | - |
| dc.date.available | 2024-11-11 | |
| dc.date.copyright | 2020-07-20 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-02 | |
| dc.identifier.citation | [1] Rose, J., Lawrence, A., et al. (2018). Bridging the trust gap in personal data. Boston Consulting Group, Boston, MA, USA, Tech. Rep. [2] Yao, A. C. (1982). Protocols for secure computations. Paper presented at the 23rd annual symposium on foundations of computer science (sfcs 1982). [3] Shamir, A. (1979). How to share a secret. Communications of the ACM, 22(11), 612-613. [4] Mohassel, P., Zhang, Y. (2017). Secureml: A system for scalable privacy- preserving machine learning. Paper presented at the 2017 IEEE Symposium on Security and Privacy (SP). [5] Li, J., Wang, N., et al. (2018). New secret sharing scheme based on faster R-CNNs image retrieval. IEEE Access, 6, 49348-49357. [6] McMahan, H. B., Moore, E., et al. (2016). Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629. [7] Yang, Q., Liu, Y., et al. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19. [8] Hard, A., Rao, K., et al. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604. [9] Suzumura, T., Zhou, Y., et al. (2019). Towards Federated Graph Learning for Collaborative Financial Crimes Detection. arXiv preprint arXiv:1909.12946. [10] Chen, Y., Qin, X., et al. (2020). Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems. [11]Nakamoto, S. (2019). Bitcoin: A peer-to-peer electronic cash system. Manubot. [12] Sultan, K., Ruhi, U., et al. (2018). Conceptualizing blockchains: characteristics applications. arXiv preprint arXiv:1806.03693. [13] Zheng, Z., Xie, S., et al. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375. [14] Buterin, V. (2014). A next-generation smart contract and decentralized application platform. white paper, 3(37). [15] Buterin, V., Reijsbergen, D., et al. (2019). Incentives in Ethereum’s hybrid casper protocol. Paper presented at the 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). [16] Khoury, D., Kfoury, E. F., et al. (2018). Decentralized voting platform based on ethereum blockchain. Paper presented at the 2018 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET). [17] Benet, J. (2014). Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561. [18] Ali, M. S., Dolui, K., et al. (2017). IoT data privacy via blockchains and IPFS. Paper presented at the Proceedings of the Seventh International Conference on the Internet of Things. [19] Wang, R. Y., Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 12(4), 5-33. [20] Strong, D. M., Lee, Y. W., et al. (1997). Data quality in context. Communications of the ACM, 40(5), 103-110. [21] Askham, N., Cook, D., et al. (2013). The six primary dimensions for data quality assessment. DAMA UK Working Group, 432-435. [22] Kienzler, M., Kowalkowski, C. (2017). Pricing strategy: A review of 22 years of marketing research. Journal of Business Research, 78, 101-110. [23] Heckman, J. R., Boehmer, E. L., et al. (2015). A pricing model for data markets. iConference 2015 Proceedings. [24] Stahl, F., Vossen, G. (2016). Data quality scores for pricing on data marketplaces. Paper presented at the Asian Conference on Intelligent Information and Database Systems. [25] Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux journal, 2014(239), 2. [26] Deichmann, J., Heineke, K., et al. (2016). Creating a successful Internet of Things data marketplace. McKinsey Company. [27] Stahl, F., Schomm, F., et al. (2014). The data marketplace survey revisited. Retrieved from [28] Dao, D., Alistarh, D., et al. (2018). Databright: Towards a global exchange for decentralized data ownership and trusted computation. arXiv preprint arXiv:1802.04780. [29] Özyilmaz, K. R., Doğan, M., et al. (2018). IDMoB: IoT data marketplace on blockchain. Paper presented at the 2018 Crypto Valley Conference on Blockchain Technology (CVCBT). [30] Mai, D., Hoang, K. (2013). Motorbike theft detection based on object detection and human activity recognition. Paper presented at the 2013 International Conference on Control, Automation and Information Sciences (ICCAIS). [31] Piergiovanni, A., Ryoo, M. S. (2018). Fine-grained activity recognition in baseball videos. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. [32] Mubashir, M., Shao, L., et al. (2013). A survey on fall detection: Principles and approaches. Neurocomputing, 100, 144-152. [33] WHO Facts Sheets: Fall https://www.who.int/news-room/fact-sheets/detail/falls | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61282 | - |
| dc.description.abstract | 在越來越多科技大公司爆發濫用數據醜聞後，人們開始意識到每天常用的軟體，看似免費的提供便利的服務，其實都是透過犧牲個人隱私所換來的。世界各國政府也開始意識到保障隱私的重要性，紛紛祭出嚴格的資料隱私保護法規來強化保護個人隱私，將資料所有權交還給資料擁有者。同時，付費換取資料使用權的經濟趨勢也正在興起。
這些趨勢的興起，對於運用機器學習的企業造成莫大的挑戰，使其同時面臨隱私保護法規和資料付費的挑戰，這些挑戰使得企業在日常營運中受到越來越多的限制阻礙，進而影響其企業營運。 本研究旨在建立一個分散式隱私保護的數據交易平台，透過整合聯邦學習（Federated Learning）、區塊鏈 （Blockchain）、資料品質評估（Data Quality Assessment）及容器技術 （Container Technology），提出一解決方案來解決隱私保護與數據運用的取捨挑戰。 在建構數據交易平台後，實際透過跌倒偵測（Fall Detection）具有隱私考量的案例，來測試平台的功能性、並驗證平台運用聯邦學習與資料品質評估技術的有效性。根據實驗結果顯示： （一）透過聯邦學習的方式建構跌倒預測模型能夠在 19 位資料提供者的情況下達到 95% 準確度，其表現與一般機器學習表現相當。 （二）在不同程度的資料污染情況下，資料品質評估技術皆能夠顯著提高模型準確度。 簡言之，本研究為企業端（模型需求者）與消費者端（資料提供者）提供一套具備隱私保護且可信任的數據交易平台架構，雙方皆可以在遵循隱私法規下進行隱私資料模型訓練。 | zh_TW |
| dc.description.abstract | After more and more data misused scandal from big technology companies break out, the people realize that the application they used daily appears to be free, but actually, all the conveniences are at the expense of their privacy. In response to public awareness of data privacy, governments worldwide started to enact the regulations to strengthen personal data privacy and give the data ownership back to individuals. Meanwhile, the trend of the pay-for-privacy economy is prevailing.
The enterprises conducting machine learning as their regular business are now facing challenges from both privacy regulations and data usage payment awareness. Their regular business will become more and more restricted in the near future, affecting the bottom line profoundly. This research aims to constructs a proof of concept of a decentralized privacy-preserving data marketplace to bridge the gap between enterprises and customers. The proposed data marketplace is an agglomeration of novel technologies including Federated Learning (FL), Blockchain, Data Quality Assessment (DQA), and Container Technology. We examine the usability of the proposed application with the fall detection case and conduct the effectiveness experiments on FL and DQA. According to the results: (1) the federated learning model achieves 95% accuracy with 19 engaged subjects and is comparable to centralized settings; (2) the data quality assessment process can significantly improve the model performance under different degrees of noise. In conclusion, the proposed data marketplace provides a privacy-by-design and trustworthy platform for stakeholders to trade between models and data. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:58:01Z (GMT). No. of bitstreams: 1 U0001-2906202018553100.pdf: 2953676 bytes, checksum: 442cb436499f208e37dc6aa20a7ec4ec (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 ........................................................................................................... # 誌謝....................................................................................................................................i 摘要.................................................................................................................................. ii ABSTRACT .................................................................................................................... iii LIST OF FIGURES..........................................................................................................vi LIST OF TABLES ......................................................................................................... vii Chapter 1 Introduction .............................................................................................1 1.1 Motivation ......................................................................................................... 1 1.2 Objectives .......................................................................................................... 2 1.3 Outline ............................................................................................................... 3 Chapter 2 Literature Review....................................................................................4 2.1 Privacy-preserving Machine Learning ..............................................................4 2.1.1 Secure Multi-Party Computation ..................................................................5 2.1.2 Federated Learning........................................................................................6 2.2 Blockchain.........................................................................................................8 2.2.1 Ethereum ....................................................................................................... 8 2.2.2 Smart Contract and Decentralized Application.............................................9 2.2.3 Interplanetary File System ..........................................................................10 2.3 Data Quality Assessment and Data Pricing Strategy ......................................11 2.4 Container Technology .....................................................................................13 2.5 Data Marketplace ............................................................................................14 Chapter 3 Proposed Architecture and Methodology ...........................................16 3.1 Design of Decentralized Privacy-preserving Data Marketplace ..................... 16 3.1.1 Smart Contract and Decentralized Application Design ..............................19 3.1.2 Data Quality Assessment Design ................................................................22 3.1.3 Local Execution Environment Design ........................................................23 3.1.4 Federated Learning Design .........................................................................24 3.2 Experiment with Fall Detection Data..............................................................26 3.2.1 UniMiB SHAR Dataset...............................................................................27 3.2.2 Experiment Design......................................................................................29 Chapter 4 Experiment Results ...............................................................................33 4.1 Construct the Decentralized Privacy-preserving Data Marketplace ...............33 4.1.1 Implementation of Data Marketplace..........................................................33 4.1.2 Implementation of Project Initiation ...........................................................35 4.1.3 Implementation of Project Participation .....................................................37 4.2 The Effectiveness Experiments of Federated Learning and Data Quality Assessment ..................................................................................................................40 4.2.1 The Experiments of Data Quantity: Federated Learning ............................40 4.2.2 The Experiments of Data Quality: Data Quality Assessment.....................41 Chapter 5 Conclusion Future Works ................................................................44 5.1 Conclusion.......................................................................................................44 5.2 Future Works...................................................................................................45 REFERENCE ................................................................................................................47 | |
| dc.language.iso | en | |
| dc.subject | 聯邦學習 | zh_TW |
| dc.subject | 區塊鏈 | zh_TW |
| dc.subject | 資料品質評估 | zh_TW |
| dc.subject | 容器技術 | zh_TW |
| dc.subject | 去中心化應用 | zh_TW |
| dc.subject | 隱私保護 | zh_TW |
| dc.subject | 數據交易平台 | zh_TW |
| dc.subject | Data Quality Assessment | en |
| dc.subject | Decentralized Application | en |
| dc.subject | Container Technology | en |
| dc.subject | Data Marketplace | en |
| dc.subject | Privacy-preserving | en |
| dc.subject | Federated Learning | en |
| dc.subject | Blockchain | en |
| dc.title | 基於聯邦學習與區塊鏈之分散式隱私保護數據交易平台 | zh_TW |
| dc.title | Decentralized Privacy-preserving Data Marketplace Based on Federated Learning and Blockchain | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳建錦(Chien-Chin Chen),盧信銘(Hsin-Min Lu) | |
| dc.subject.keyword | 數據交易平台,隱私保護,聯邦學習,區塊鏈,資料品質評估,容器技術,去中心化應用, | zh_TW |
| dc.subject.keyword | Data Marketplace,Privacy-preserving,Federated Learning,Blockchain,Data Quality Assessment,Container Technology,Decentralized Application, | en |
| dc.relation.page | 50 | |
| dc.identifier.doi | 10.6342/NTU202001197 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-07-02 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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