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1Dissertation/ Thesis
المؤلفون: Waldner, Hendrik
المساهمون: Kiayias, Aggelos, Kohlweiss, Markulf, Zikas, Vassilis
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2Academic Journal
المؤلفون: Gentile, Lorenzo, David, Bernardo, Brorsson, Joakim, Stankovski Wagner, Paul, Pagnin, Elena
المصدر: Gentile , L , David , B , Brorsson , J , Stankovski Wagner , P & Pagnin , E 2023 , ' PAPR: Publicly Auditable Privacy Revocation for Anonymous Credentials ' , CT-RSA 2023 The Cryptographers' Track at RSA Conference . < https://eprint.iacr.org/2023/137.pdf >
مصطلحات موضوعية: Anonymous Credentials, Publicly Auditable Privacy Revocation, Conditional User Privacy, Privacy Revocation Accountability, Universal Composability Framework
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3Academic Journal
المؤلفون: Ilya M. Shilov, Danil A. Zakoldaev
المصدر: Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki, Vol 21, Iss 2, Pp 249-255 (2021)
مصطلحات موضوعية: blockchain, robust distributed ledger, multidimensional blockchain, universal composability framework, consensus mechanism, transactions, security proof, Optics. Light, QC350-467, Electronic computers. Computer science, QA75.5-76.95
Relation: https://ntv.ifmo.ru/file/article/20340.pdf; https://doaj.org/toc/2226-1494; https://doaj.org/toc/2500-0373; https://doaj.org/article/11ceb7bb5a3146c999c3d9294059dc60
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4Dissertation/ Thesis
المؤلفون: 蕭守晴, Hsiao, Shou-Ching
المساهمون: 左瑞麟, Tso, Ray-Lin
مصطلحات موضوعية: 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
وصف الملف: 4667550 bytes; application/pdf
Relation: [1] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep\nlearning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on\nComputer and Communications Security, pages 308–318, 2016.\n[2] A. F. Agarap and F. J. H. Pepito. Towards Building an Intelligent Anti-Malware System: A Deep\nLearning Approach using Support Vector Machine (SVM) for Malware Classification. arXiv\npreprint arXiv:1801.00318, 2017.\n[3] G. Beigi, K. Shu, R. Guo, S. Wang, and H. Liu. Privacy Preserving Text Representation Learning.\nProceedings of the 30th on Hypertext and Social Media (HT’19). ACM, 2019.\n[4] S. Biswas, E. Chadda, and F. Ahmad. Sentiment Analysis with Gated Recurrent Units. Department\nof Computer Engineering. Annual Report Jamia Millia Islamia New Delhi, India, 2015.\n[5] G. R. Blakley. Safeguarding cryptographic keys. In 1979 International Workshop on Managing\nRequirements Knowledge (MARK), pages 313–318. IEEE, 1979.\n[6] R. Canetti. Security and Composition of Multiparty Cryptographic Protocols. Journal of CRYPTOLOGY,\n13(1):143–202, 2000.\n[7] R. Canetti. Universally Composable Security: A New Paradigm for Cryptographic Protocols.\nIn Proceedings 42nd IEEE Symposium on Foundations of Computer Science, pages 136–145.\nIEEE, 2001.\n[8] R. Canetti. Security and composition of cryptographic protocols: a tutorial (part i). ACM SIGACT\nNews, 37(3):67–92, 2006.\n[9] R. Canetti, A. Cohen, and Y. Lindell. A Simpler Variant of Universally Composable Security\nfor Standard Multiparty Computation. In Annual Cryptology Conference, pages 3–22. Springer,\n2015.\n[10] T. Capes, P. Coles, A. Conkie, L. Golipour, A. Hadjitarkhani, Q. Hu, N. Huddleston, M. Hunt,\nJ. Li, M. Neeracher, et al. Siri On-Device Deep Learning-Guided Unit Selection Text-to-Speech\nSystem. In INTERSPEECH, pages 4011–4015, 2017.\n[11] H. Chabanne, A. de Wargny, J. Milgram, C. Morel, and E. Prouff. Privacy-preserving Classification\non Deep Neural Network. IACR Cryptology ePrint Archive, 2017:35, 2017.\n[12] C.-C. Chiu, T. N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R. J.\nWeiss, K. Rao, E. Gonina, et al. State-of-the-art Speech Recognition with Sequence-to-sequence\nModels. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing\n(ICASSP), pages 4774–4778. IEEE, 2018.\n[13] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio.\nLearning Phrase Representations using RNN Encoder-decoder for Statistical Machine\nTranslation. arXiv preprint arXiv:1406.1078, 2014.\n[14] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical Evaluation of Gated Recurrent Neural\nNetworks on Sequence Modeling. arXiv preprint arXiv:1412.3555, 2014.\n[15] M. De Cock, R. Dowsley, A. C. Nascimento, D. Reich, and A. Todoki. Privacy-Preserving\nClassification of Personal Text Messages with Secure Multi-Party Computation: An Application\nto Hate-Speech Detection. arXiv preprint arXiv:1906.02325, 2019.\n[16] W. Diffie and M. Hellman. New Directions in Cryptography. IEEE transactions on Information\nTheory, 22(6):644–654, 1976.\n[17] W. Du and M. J. Atallah. Protocols for Secure Remote Database Access with Approximate\nMatching. In E-Commerce Security and Privacy, pages 87–111. Springer, 2001.\n[18] C. Dwork. Differential Privacy. Encyclopedia of Cryptography and Security, pages 338–340,\n2011.\n[19] M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart. Privacy in pharmacogenetics:\nAn end-to-end case study of personalized warfarin dosing. In 23rd fUSENIXg Security\nSymposium (fUSENIXg Security 14), pages 17–32, 2014.\n[20] R. Fu, Z. Zhang, and L. Li. Using LSTM and GRU Neural Network Methods for Traffic Flow\nPrediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation\n(YAC), pages 324–328. IEEE, 2016.\n[21] R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing. Cryptonets:\nApplying neural networks to encrypted data with high throughput and accuracy. In International\nConference on Machine Learning, pages 201–210, 2016.\n[22] O. Goldreich. Foundations of Cryptography: volume 1, basic tools. Cambridge university press,\n2007.\n[23] O. Goldreich. Foundations of cryptography: volume 2, basic applications. Cambridge university\npress, 2009.\n[24] O. Goldreich, S. Micali, and A. Wigderson. How to Play Any Mental Game, or A Completeness\nTheorem for Protocols with Honest Majority. In Providing Sound Foundations for Cryptography:\nOn the Work of Shafi Goldwasser and Silvio Micali, pages 307–328. 2019.\n[25] Q. Gu, N. Lu, and L. Liu. A Novel Recurrent Neural Network Algorithm with Long Short-term\nMemory Model for Futures Trading. Journal of Intelligent & Fuzzy Systems, 37(4):1–8.\n[26] X. Hu, L. Liang, L. Deng, S. Li, X. Xie, Y. Ji, Y. Ding, C. Liu, T. Sherwood, and Y. Xie. Neural\nnetwork model extraction attacks in edge devices by hearing architectural hints. arXiv preprint\narXiv:1903.03916, 2019.\n[27] Y. Huang. Practical Secure Two-party Computation. PhD thesis, Citeseer, 2012.\n[28] T. Hunt, C. Song, R. Shokri, V. Shmatikov, and E. Witchel. Chiron: Privacy-preserving Machine\nLearning as a Service. arXiv preprint arXiv:1803.05961, 2018.\n[29] Z. Ji, Z. C. Lipton, and C. Elkan. Differential Privacy and Machine Learning: A Survey and\nReview. arXiv preprint arXiv:1412.7584, 2014.\n[30] C. Juvekar, V. Vaikuntanathan, and A. Chandrakasan. GAZELLE: A Low Latency Framework\nfor Secure Neural Network Inference. In 27th USENIX Security Symposium (USENIX Security\n18), pages 1651–1669, 2018.\n[31] R. Küsters and D. Rausch. A framework for universally composable diffie-hellman key exchange.\nIn 2017 IEEE Symposium on Security and Privacy (SP), pages 881–900. IEEE, 2017.\n[32] R. Küsters and M. Tuengerthal. Universally composable symmetric encryption. In 2009 22nd\nIEEE Computer Security Foundations Symposium, pages 293–307. IEEE, 2009.\n[33] Y. Li, T. Baldwin, and T. Cohn. Towards Robust and Privacy-preserving Text Representations.\narXiv preprint arXiv:1805.06093, 2018.\n[34] Y. Lindell. How to Simulate It–A Tutorial on the Simulation Proof Technique. In Tutorials on\nthe Foundations of Cryptography, pages 277–346. Springer, 2017.\n[35] J. Liu, M. Juuti, Y. Lu, and N. Asokan. Oblivious Neural Network Predictions via Minionn\nTransformations. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications\nSecurity, pages 619–631. ACM, 2017.\n[36] L. Ma, S. Liu, and Y. Wang. A DRM model based on Proactive Secret Sharing Scheme for P2P\nNetworks. In 9th IEEE International Conference on Cognitive Informatics (ICCI’10), pages\n859–862. IEEE, 2010.\n[37] A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. Learning Word Vectors\nfor Sentiment Analysis. In Proceedings of the 49th annual meeting of the association for computational\nlinguistics: Human language technologies, volume 1, pages 142–150. Association for\nComputational Linguistics, 2011.\n[38] P. Mohassel and Y. Zhang. Secureml: A System for Scalable Privacy-preserving Machine Learning.\nIn 2017 IEEE Symposium on Security and Privacy (SP), pages 19–38. IEEE, 2017.\n[39] T. B. Pedersen, Y. Saygın, and E. Savaş. Secret Sharing vs. Encryption-based Techniques for\nPrivacy Preserving Data Mining. 2007.\n[40] P. Poomka, W. Pongsena, N. Kerdprasop, and K. Kerdprasop. Sms spam detection based on\nlong short-term memory and gated recurrent unit. International Journal of Future Computer\nand Communication, 8(1), 2019.\n[41] M. S. Riazi, C. Weinert, O. Tkachenko, E. M. Songhori, T. Schneider, and F. Koushanfar.\nChameleon: A Hybrid Secure Computation Framework for Machine Learning Applications. In\nProceedings of the 2018 on Asia Conference on Computer and Communications Security, pages\n707–721. ACM, 2018.\n[42] M. Ribeiro, K. Grolinger, and M. A. Capretz. Mlaas: Machine learning as a service. In 2015\nIEEE 14th International Conference on Machine Learning and Applications (ICMLA), pages\n896–902. IEEE, 2015.\n[43] V. Rijmen and J. Daemen. Advanced encryption standard. Proceedings of Federal Information\nProcessing Standards Publications, National Institute of Standards and Technology, pages 19–\n22, 2001.\n[44] R. L. Rivest, L. Adleman, M. L. Dertouzos, et al. On Data Banks and Privacy Homomorphisms.\nFoundations of secure computation, 4(11):169–180, 1978.\n[45] B. D. Rouhani, M. S. Riazi, and F. Koushanfar. Deepsecure: Scalable Provably-secure Deep\nLearning. In Proceedings of the 55th Annual Design Automation Conference, page 2. ACM,\n2018.\n[46] S. van der Walt, S.C. Colbert, and G. Varoquaux. The NumPy Array: A Structure for Efficient\nNumerical Computation. Computing in Science Engineering, 13(2):22–30, March 2011.\n[47] N. Saleem, M. Irfan Khattak, and A. B. Qazi. Supervised Speech Enhancement based on Deep\nNeural Network. Journal of Intelligent & Fuzzy Systems, 37(4):5187–5201, 2019.\n[48] A. Shamir. How to Share a Secret. Communications of the ACM, 22(11):612–613, 1979.\n[49] D. Takabi, R. Podschwadt, J. Druce, C. Wu, and K. Procopio. Privacy Preserving Neural Network\nInference on Encrypted Data with GPUs. arXiv preprint arXiv:1911.11377, 2019.\n[50] S. Wagh, D. Gupta, and N. Chandran. SecureNN: 3-Party Secure Computation for Neural Network\nTraining. Proceedings on Privacy Enhancing Technologies, 1:24, 2019.\n[51] L. Wang, X. Shen, J. Li, J. Shao, and Y. Yang. Cryptographic Primitives in Blockchains. Journal\nof Network and Computer Applications, 127:43–58, 2019.\n[52] A. C.-C. Yao. How to Generate and Exchange Secrets. In 27th Annual Symposium on Foundations\nof Computer Science (SFCS 1986), pages 162–167. IEEE, 1986.\n[53] W. Yin, K. Kann, M. Yu, and H. Schütze. Comparative Study of CNN and RNN for Natural\nLanguage Processing. arXiv preprint arXiv:1702.01923, 2017.\n[54] Z. Ying, S. Cao, P. Zhou, S. Zhang, and X. Liu. Lightweight outsourced privacy-preserving heart\nfailure prediction based on gru. In International Conference on Algorithms and Architectures\nfor Parallel Processing, pages 521–536. Springer, 2019.\n[55] A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola. Dive into Deep Learning. 2020. https:\n//d2l.ai.; G0107753010; https://nccur.lib.nccu.edu.tw//handle/140.119/131633; https://nccur.lib.nccu.edu.tw/bitstream/140.119/131633/1/301001.pdf