Fetching the paper…
Reading the bibliography…
AI foundation models have recently demonstrated impressive capabilities across a wide range of tasks.
P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in International Conference on the Theory and Applications of Cryptographic Techniques (Eurocrypt) . Springer, 1999
1999
Earlier work this paper cites.
C. P. Schnorr, “Lattice reduction by random sampling and birthday methods,” in Annual Symposium on Theoretical Aspects of Computer Science . Springer, 2003, pp. 145–156
2003
Earlier work this paper cites.
O. Goldreich, Foundations of Cryptography, Volume 2 . Cambridge University Press, 2004
2004
Earlier work this paper cites.
A. Cambini and L. Martein, Generalized convexity and optimization: Theory and applications . Springer Science & Business Media, 2008
2008
Earlier work this paper cites.
C. Gentry, A fully homomorphic encryption scheme . Stanford university, 2009
2009
Earlier work this paper cites.
Z. Brakerski, “Fully homomorphic encryption without modulus switching from classical GapSVP,” in Annual Cryptology Conference . Springer, 2012, pp. 868–886
2012
Earlier work this paper cites.
Y. Jong, “An efficient global optimization algorithm for nonlinear sum-of-ratios problem,” Optimization Online , pp. 1–21, 2012
2012
Earlier work this paper cites.
M. Liu and P. Q. Nguyen, “Solving BDD by enumeration: An update,” in Cryptographers’ Track at the RSA Conference . Springer, 2013
2013
Earlier work this paper cites.
Z. Brakerski, C. Gentry, and V. Vaikuntanathan, “(leveled) fully homomorphic encryption without bootstrapping,” ACM Transactions on Computation Theory (TOCT) , vol. 6, no. 3, pp. 1–36, 2014
2014
Earlier work this paper cites.
M. R. Albrecht, R. Player, and S. Scott, “On the concrete hardness of learning with errors,” Journal of Mathematical Cryptology , 2015
2015
Earlier work this paper cites.
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 . PMLR, 2016, pp. 201–210
2016
Earlier work this paper cites.
J. H. Cheon, A. Kim, M. Kim, and Y. Song, “Homomorphic encryption for arithmetic of approximate numbers,” in ASIACRYPT , 2017, pp. 409–437
2017
Earlier work this paper cites.
M. R. Albrecht, “On dual lattice attacks against small-secret lwe and parameter choices in helib and seal,” in Annual International Conference on the Theory and Applications of Cryptographic Techniques . Springer, 2017, pp. 103–129
2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI Blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
M. Hoang, O. A. Bihorac, and J. Rouces, “Aspect-based sentiment analysis using BERT,” in Nordic Conference on Computational Linguistics , 2019, pp. 187–196
2019
Earlier work this paper cites.
Q. Wang, P. Liu, Z. Zhu, H. Yin, Q. Zhang, and L. Zhang, “A text abstraction summary model based on BERT word embedding and reinforcement learning,” Applied Sciences , vol. 9, no. 21, p. 4701, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. Jin, A. Al Badawi, J. Unnikrishnan, C. F. Mun, J. M. Brown, J. P. Campbell, M. Chiang, J. Kalpathy-Cramer, V. R. Chandrasekhar, P. Krishnaswamy et al. , “CareNets: Efficient homomorphic cnn for high resolution images,” in NeurIPS Workshop on Privacy in Machine Learning (PriML) , 2019
2019
Earlier work this paper cites.
Q. Lou and L. Jiang, “SHE: A fast and accurate deep neural network for encrypted data,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, 2019
2019
Earlier work this paper cites.
J. H. Cheon, K. Han, A. Kim, M. Kim, and Y. Song, “A full RNS variant of approximate homomorphic encryption,” in Selected Areas in Cryptography–SAC 2018: 25th International Conference, 2018, Revised Selected Papers 25 . Springer, 2019, pp. 347–368
2019
Cited alongside, same era.
N. J. H. Marcano, M. Moller, S. Hansen, and R. H. Jacobsen, “On fully homomorphic encryption for privacy-preserving deep learning,” in 2019 IEEE Globecom Workshops (GC Wkshps) , 2019
2019
Cited alongside, same era.
S. Kumari and A. Singh, “Fair end-to-end window-based congestion control in time-varying data communication networks,” International Journal of Communication Systems , vol. 32, 2019
2019
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Later among the works it cites.
Y. Dai, M. de Kamps, and S. Sharoff, “BERTology for machine translation: What BERT knows about linguistic difficulties for translation,” in Language Resources and Evaluation Conference , 2022, pp. 6674–6690
2022
Later among the works it cites.
J.-W. Lee, H. Kang, Y. Lee, W. Choi, J. Eom, M. Deryabin, E. Lee, J. Lee, D. Yoo, Y.-S. Kim et al. , “Privacy-preserving machine learning with fully homomorphic encryption for deep neural network,” IEEE Access , 2022
2022
Later among the works it cites.
Z. Bu, Y.-X. Wang, S. Zha, and G. Karypis, “Differentially private bias-term only fine-tuning of foundation models,” in Neural Information Processing Systems (NeurIPS) Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML) , 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Al Badawi, L. Hoang, C. F. Mun, K. Laine, and K. M. M. Aung, “Privft: Private and fast text classification with homomorphic encryption,” IEEE Access , 2020
2020
Cited alongside, same era.
S. Abuadbba, K. Kim, M. Kim, C. Thapa, S. A. Camtepe, Y. Gao, H. Kim, and S. Nepal, “Can we use split learning on 1D CNN models for privacy preserving training?” in ASIACCS , 2020, pp. 305–318
2020
Cited alongside, same era.
A. Boulemtafes, A. Derhab, and Y. Challal, “A review of privacy-preserving techniques for deep learning,” Neurocomputing , 2020
2020
Cited alongside, same era.
T. Suzuki, Y. Ishimaki, and H. Yamana, “Damcrem: Dynamic allocation method of computation resource to macro-tasks for fully homomorphic encryption applications,” in IEEE International Conference on Smart Computing (SMARTCOMP) , 2020, pp. 458–463
2020
Cited alongside, same era.
C. T. Dinh, N. H. Tran, M. N. Nguyen, C. S. Hong, W. Bao, A. Y. Zomaya, and V. Gramoli, “Federated learning over wireless networks: Convergence analysis and resource allocation,” IEEE/ACM Transactions on Networking , vol. 29, no. 1, pp. 398–409, 2020
2020
Cited alongside, same era.
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” in International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
R. Podschwadt, D. Takabi, P. Hu, M. H. Rafiei, and Z. Cai, “A survey of deep learning architectures for privacy-preserving machine learning with fully homomorphic encryption,” IEEE Access , 2022
2022
Later among the works it cites.
J. Jang, Y. Lee, A. Kim, B. Na, D. Yhee, B. Lee, J. H. Cheon, and S. Yoon, “Privacy-preserving deep sequential model with matrix homomorphic encryption,” in ACM on Asia Conference on Computer and Communications Security (AsiaCCS) , 2022, pp. 377–391
2022
Later among the works it cites.
M. Hao, H. Li, H. Chen, P. Xing, G. Xu, and T. Zhang, “Iron: Private inference on transformers,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 35, pp. 15 718–15 731, 2022
2022
Later among the works it cites.
R. T. Zoppei, M. A. Delgado, L. H. Macedo, M. J. Rider, and R. Romero, “A branch and bound algorithm for transmission network expansion planning using nonconvex mixed-integer nonlinear programming models,” IEEE Access , vol. 10, pp. 39 875–39 888, 2022
2022
Later among the works it cites.
X. Zhou, C. Liu, and J. Zhao, “Resource allocation of federated learning for the metaverse with mobile augmented reality,” IEEE Transactions on Wireless Communications , 2023
2023
Later among the works it cites.
A. K. Srivastawa, “Exploring contract management in the digital age: The impact of artificial intelligence,” Jus Corpus Law Journal , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
P. Zmushko, M. Mansurov, R. Svirschevski, D. Kuznedelev, M. Ryabinin, and A. Beznosikov, “Privacy preserving API fine-tuning for LLMs,” September 2023. [Online]. Available: https://openreview.net/forum?id=jMJ9IRWmH9
2023
Later among the works it cites.
Q. Pang, J. Zhu, H. Möllering, W. Zheng, and T. Schneider, “Bolt: Privacy-preserving, accurate and efficient inference for transformers,” Cryptology ePrint Archive , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
C. C. Sai Balne, S. Bhaduri, T. Roy, V. Jain, and A. Chadha, “Parameter efficient fine tuning: A comprehensive analysis across applications,” arXiv e-prints , pp. arXiv–2404, 2024
2024
Closest in time.
2024
Closest in time.