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題名 結合隱私保護功能之GRU預測模型框架
A Study on Privacy-preserving GRU Inference Framework
作者 蕭守晴
Hsiao, Shou-Ching
貢獻者 左瑞麟
Tso, Ray-Lin
蕭守晴
Hsiao, Shou-Ching
關鍵詞 隱私保護
Gated Recurrent Unit模型
秘密分享
Universal Composability架構
Privacy-preserving
Gated Recurrent Unit Model
Secret Sharing
Universal Composability Framework
日期 2020
上傳時間 2-九月-2020 12:15:48 (UTC+8)
摘要 Gated Recurrent Unit (GRU) 模型具有廣泛應用，包括情緒分析、語音辨識、惡意程式分析等領域。在提供服務階段，模型擁有者常選擇雲端機器學習服務 (Machine-learning-as-a-service, MLaaS) 作為系統架構，因其提供企業以低建置成本部屬模型且達到高效能機器學習服務；然而，資料上傳至雲端會產生隱私疑慮，包括模型隱私、使用者資料隱私以及預測結果隱私，無論是雲端代管商遭受外部入侵或內部員工竊取，都有可能造成隱私洩漏。本篇研究主要針對含有隱私資料的預測情境，如文字資料、網路封包資料、醫療心電圖等資料，並選用能學習時序關聯性的 GRU 模型來設計隱私保護預測框架。考量系統的準確度與效能，本文採用秘密分享 (Secret Sharing) 機制作為主要保護隱私方式，並設計基於秘密分享的 GRU 系統架構與演算法。由於所有雲端上的運算都針對分享秘密 (Secret Shares) 進行，任何一方都無法從部分秘密得知原本的模型參數、預測資料及預測結果，其安全性在半誠實攻擊者模型下可透過Universal Composability證明，並確保能安全地套用至不同架構之 GRU 模型。除此之外，本文也透過實作證實架構與演算法的正確性，並分別以時間與準確度呈現實驗結果。
Gated Recurrent Unit (GRU) has broad application fields, such as sentiment analysis, speech recognition, malware analysis, and other sequential data processing. For low-cost deployment and efficient machine learning services, a growing number of model owners choose to deploy the trained GRU models through Machine-learning-as-a-service (MLaaS). However, privacy has become a significant concern for both model owners and prediction clients, including model weights privacy, input data privacy, and output results privacy. The privacy leakage may be caused by either external intrusion or insider attacks. To address the above issues, this research designs a framework for privacy-preserving GRU models, which aims for privacy scenarios such as predicting on textual data, network packets, heart rate data, and so on. In consideration of accuracy and efficiency, this research uses additive secret sharing to design the basic operations and gating mechanisms of GRU. The protocols can meet the security requirements of privacy and correctness under the Universal Composability framework with the semi-honest adversary. Additionally, the framework and protocols are realized with a proof-of-concept implementation. The experimental results are presented with respect to time consumption and inference accuracy.
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Models. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing
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[13] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio.
Learning Phrase Representations using RNN Encoder-decoder for Statistical Machine
Translation. arXiv preprint arXiv:1406.1078, 2014.
[14] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical Evaluation of Gated Recurrent Neural
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[15] M. De Cock, R. Dowsley, A. C. Nascimento, D. Reich, and A. Todoki. Privacy-Preserving
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描述 碩士
國立政治大學
資訊科學系
107753010
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107753010
資料類型 thesis
dc.contributor.advisor 左瑞麟zh_TW
dc.contributor.advisor Tso, Ray-Linen_US
dc.contributor.author (作者) 蕭守晴zh_TW
dc.contributor.author (作者) Hsiao, Shou-Chingen_US
dc.creator (作者) 蕭守晴zh_TW
dc.creator (作者) Hsiao, Shou-Chingen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-九月-2020 12:15:48 (UTC+8)-
dc.date.available 2-九月-2020 12:15:48 (UTC+8)-
dc.date.issued (上傳時間) 2-九月-2020 12:15:48 (UTC+8)-
dc.identifier (其他 識別碼) G0107753010en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131633-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 107753010zh_TW
dc.description.abstract (摘要) Gated Recurrent Unit (GRU) 模型具有廣泛應用，包括情緒分析、語音辨識、惡意程式分析等領域。在提供服務階段，模型擁有者常選擇雲端機器學習服務 (Machine-learning-as-a-service, MLaaS) 作為系統架構，因其提供企業以低建置成本部屬模型且達到高效能機器學習服務；然而，資料上傳至雲端會產生隱私疑慮，包括模型隱私、使用者資料隱私以及預測結果隱私，無論是雲端代管商遭受外部入侵或內部員工竊取，都有可能造成隱私洩漏。本篇研究主要針對含有隱私資料的預測情境，如文字資料、網路封包資料、醫療心電圖等資料，並選用能學習時序關聯性的 GRU 模型來設計隱私保護預測框架。考量系統的準確度與效能，本文採用秘密分享 (Secret Sharing) 機制作為主要保護隱私方式，並設計基於秘密分享的 GRU 系統架構與演算法。由於所有雲端上的運算都針對分享秘密 (Secret Shares) 進行，任何一方都無法從部分秘密得知原本的模型參數、預測資料及預測結果，其安全性在半誠實攻擊者模型下可透過Universal Composability證明，並確保能安全地套用至不同架構之 GRU 模型。除此之外，本文也透過實作證實架構與演算法的正確性，並分別以時間與準確度呈現實驗結果。zh_TW
dc.description.abstract (摘要) Gated Recurrent Unit (GRU) has broad application fields, such as sentiment analysis, speech recognition, malware analysis, and other sequential data processing. For low-cost deployment and efficient machine learning services, a growing number of model owners choose to deploy the trained GRU models through Machine-learning-as-a-service (MLaaS). However, privacy has become a significant concern for both model owners and prediction clients, including model weights privacy, input data privacy, and output results privacy. The privacy leakage may be caused by either external intrusion or insider attacks. To address the above issues, this research designs a framework for privacy-preserving GRU models, which aims for privacy scenarios such as predicting on textual data, network packets, heart rate data, and so on. In consideration of accuracy and efficiency, this research uses additive secret sharing to design the basic operations and gating mechanisms of GRU. The protocols can meet the security requirements of privacy and correctness under the Universal Composability framework with the semi-honest adversary. Additionally, the framework and protocols are realized with a proof-of-concept implementation. The experimental results are presented with respect to time consumption and inference accuracy.en_US
dc.description.tableofcontents 1 Introduction 1
1.1 Motivations and Purposes 2
1.2 Contributions 3
2 Definitions and Preliminaries 5
2.1 Additive Secret Sharing (ASS) 5
2.2 Gated Recurrent Unit (GRU) Model 7
2.3 Universal Composability (UC) Framework 8
3 Technical Literature 11
3.1 Privacy-preserving Techniques 11
3.2 Privacy-preserving Deep Neural Network 12
4 Privacy-preserving GRU Inference Framework 15
4.1 Architecture 15
4.2 Security Model 16
4.2.1 Non-colluding Cloud Servers 17
4.2.2 Prediction Clients 17
4.2.3 Outsiders 17
4.2.4 Network Transmission 17
4.3 Basic Protocols 18
4.3.1 Hadamard Product 19
4.3.2 Division 21
4.3.3 Share Re-generation 22
4.3.4 Sigmoid Activation Function 22
4.3.5 Tanh Activation Function 24
4.4 Gating Protocols 25
4.4.1 Update Gate and Reset Gate 25
4.4.2 Current Memory 26
4.4.3 Activation of Current Cell 26
4.5 Putting It All Together 27
5 Security Analysis 30
5.1 Security of Basic Protocols 31
5.2 Security of Gating Protocols 39
5.3 Security of GRU Inference 41
6 Experiments and Results 45
6.1 Dataset 45
6.2 Implementation 45
6.3 Results 46
7 Discussions and Future Works 50
7.1 Discussions on Accuracy 50
7.2 Discussions on Time Consumption 51
7.3 Potential Collusion Problems 52
7.4 Extended Future Works 53
8 Conclusion 54
Reference 55		zh_TW
dc.format.extent 4667550 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107753010en_US
dc.subject (關鍵詞) 隱私保護zh_TW
dc.subject (關鍵詞) Gated Recurrent Unit模型zh_TW
dc.subject (關鍵詞) 秘密分享zh_TW
dc.subject (關鍵詞) Universal Composability架構zh_TW
dc.subject (關鍵詞) Privacy-preservingen_US
dc.subject (關鍵詞) Gated Recurrent Unit Modelen_US
dc.subject (關鍵詞) Secret Sharingen_US
dc.subject (關鍵詞) Universal Composability Frameworken_US
dc.title (題名) 結合隱私保護功能之GRU預測模型框架zh_TW
dc.title (題名) A Study on Privacy-preserving GRU Inference Frameworken_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep
learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on
Computer and Communications Security, pages 308–318, 2016.
[2] A. F. Agarap and F. J. H. Pepito. Towards Building an Intelligent Anti-Malware System: A Deep
Learning Approach using Support Vector Machine (SVM) for Malware Classification. arXiv
preprint arXiv:1801.00318, 2017.
[3] G. Beigi, K. Shu, R. Guo, S. Wang, and H. Liu. Privacy Preserving Text Representation Learning.
Proceedings of the 30th on Hypertext and Social Media (HT’19). ACM, 2019.
[4] S. Biswas, E. Chadda, and F. Ahmad. Sentiment Analysis with Gated Recurrent Units. Department
of Computer Engineering. Annual Report Jamia Millia Islamia New Delhi, India, 2015.
[5] G. R. Blakley. Safeguarding cryptographic keys. In 1979 International Workshop on Managing
Requirements Knowledge (MARK), pages 313–318. IEEE, 1979.
[6] R. Canetti. Security and Composition of Multiparty Cryptographic Protocols. Journal of CRYPTOLOGY,
13(1):143–202, 2000.
[7] R. Canetti. Universally Composable Security: A New Paradigm for Cryptographic Protocols.
In Proceedings 42nd IEEE Symposium on Foundations of Computer Science, pages 136–145.
IEEE, 2001.
[8] R. Canetti. Security and composition of cryptographic protocols: a tutorial (part i). ACM SIGACT
News, 37(3):67–92, 2006.
[9] R. Canetti, A. Cohen, and Y. Lindell. A Simpler Variant of Universally Composable Security
for Standard Multiparty Computation. In Annual Cryptology Conference, pages 3–22. Springer,
2015.
[10] T. Capes, P. Coles, A. Conkie, L. Golipour, A. Hadjitarkhani, Q. Hu, N. Huddleston, M. Hunt,
J. Li, M. Neeracher, et al. Siri On-Device Deep Learning-Guided Unit Selection Text-to-Speech
System. In INTERSPEECH, pages 4011–4015, 2017.
[11] H. Chabanne, A. de Wargny, J. Milgram, C. Morel, and E. Prouff. Privacy-preserving Classification
on Deep Neural Network. IACR Cryptology ePrint Archive, 2017:35, 2017.
[12] C.-C. Chiu, T. N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R. J.
Weiss, K. Rao, E. Gonina, et al. State-of-the-art Speech Recognition with Sequence-to-sequence
Models. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pages 4774–4778. IEEE, 2018.
[13] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio.
Learning Phrase Representations using RNN Encoder-decoder for Statistical Machine
Translation. arXiv preprint arXiv:1406.1078, 2014.
[14] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical Evaluation of Gated Recurrent Neural
Networks on Sequence Modeling. arXiv preprint arXiv:1412.3555, 2014.
[15] M. De Cock, R. Dowsley, A. C. Nascimento, D. Reich, and A. Todoki. Privacy-Preserving
Classification of Personal Text Messages with Secure Multi-Party Computation: An Application
to Hate-Speech Detection. arXiv preprint arXiv:1906.02325, 2019.
[16] W. Diffie and M. Hellman. New Directions in Cryptography. IEEE transactions on Information
Theory, 22(6):644–654, 1976.
[17] W. Du and M. J. Atallah. Protocols for Secure Remote Database Access with Approximate
Matching. In E-Commerce Security and Privacy, pages 87–111. Springer, 2001.
[18] C. Dwork. Differential Privacy. Encyclopedia of Cryptography and Security, pages 338–340,
2011.
[19] M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart. Privacy in pharmacogenetics:
An end-to-end case study of personalized warfarin dosing. In 23rd fUSENIXg Security
Symposium (fUSENIXg Security 14), pages 17–32, 2014.
[20] R. Fu, Z. Zhang, and L. Li. Using LSTM and GRU Neural Network Methods for Traffic Flow
Prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation
(YAC), pages 324–328. IEEE, 2016.
[21] R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing. Cryptonets:
Applying neural networks to encrypted data with high throughput and accuracy. In International
Conference on Machine Learning, pages 201–210, 2016.
[22] O. Goldreich. Foundations of Cryptography: volume 1, basic tools. Cambridge university press,
2007.
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dc.identifier.doi (DOI) 10.6814/NCCU202001474en_US