Built on

Y. Liu, Y. Kang, X. Zhang, L. Li, Y. Cheng, T. Chen, M. Hong, and Q. Yang, “A communication efficient vertical federated learning framework,” CoRR , vol. abs/1912.11187, 2019. [Online]. Available: http://arxiv.org/abs/1912.11187
Original
1912
Earlier work this paper cites.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konecný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao, “Advances and open problems in federated learning,” CoRR , vol. abs/1912.04977, 2019. [Online]. Available: http://arxiv.org/abs/1912.04977
Original
1912
Earlier work this paper cites.
P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in International Conference on the Theory and Applications of Cryptographic Techniques . Springer, 1999, pp. 223–238
1999
Earlier work this paper cites.
G. Liang and S. S. Chawathe, “Privacy-preserving inter-database operations,” in International Conference on Intelligence and Security Informatics . Springer, 2004, pp. 66–82
2004
Earlier work this paper cites.
J. Vaidya and C. Clifton, “Privacy-preserving decision trees over vertically partitioned data,” in IFIP Annual Conference on Data and Applications Security and Privacy . Springer, 2005, pp. 139–152
2005
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International Conference on Theory and Applications of Models of Computation . Springer, 2008, pp. 1–19
2008
Earlier work this paper cites.
J. Vaidya, “A survey of privacy-preserving methods across vertically partitioned data,” in Privacy-preserving data mining . Springer, 2008, pp. 337–358
2008
Earlier work this paper cites.
J. Vaidya, C. Clifton, M. Kantarcioglu, and A. S. Patterson, “Privacy-preserving decision trees over vertically partitioned data,” ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 2, no. 3, p. 14, 2008
2008
Earlier work this paper cites.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
V. Mayer-Schonberger and Y. Padova, “Regime change: Enabling big data through europe’s new data protection regulation,” Colum. Sci. & Tech. L. Rev. , vol. 17, p. 315, 2015
2015
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . ACM, 2015, pp. 1310–1321
2015
Earlier work this paper cites.
R. Bost, R. A. Popa, S. Tu, and S. Goldwasser, “Machine learning classification over encrypted data,” in 22nd Annual Network and Distributed System Security Symposium, NDSS , 2015
2015
Earlier work this paper cites.

Similar

P. Regulation, “The general data protection regulation,” European Commission. Available at: https://eur-lex. europa. eu/legal-content/EN/TXT , 2016
2016
Cited alongside, same era.
J. P. Albrecht, “How the gdpr will change the world,” Eur. Data Prot. L. Rev. , vol. 2, p. 287, 2016
2016
Cited alongside, same era.
B. Goodman and S. Flaxman, “European union regulations on algorithmic decision-making and a” right to explanation”,” arXiv preprint arXiv:1606.08813 , 2016
Original
2016
Cited alongside, same era.
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016
Original
2016
Cited alongside, same era.
KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York, NY, USA: Association for Computing Machinery, 2016

Then

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127 , 2018
Original
2018
Later among the works it cites.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology , vol. 10, no. 2, pp. 12:1–12:19, 2019
2019
Closest in time.
Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, and H. Yu, “Federated learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning , vol. 13, no. 3, pp. 1–207, 2019
2019
Closest in time.
M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning . PMLR, 2019, pp. 4615–4625
2019
Closest in time.

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Reading the bibliography…

2019

SecureBoost: A Lossless Federated Learning Framework

Cheng, Kewei, Fan, Tao, Jin, Yilun et al.

Understand

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018.

The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy.
To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning.
SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy.

Built on

Y. Liu, Y. Kang, X. Zhang, L. Li, Y. Cheng, T. Chen, M. Hong, and Q. Yang, “A communication efficient vertical federated learning framework,” CoRR , vol. abs/1912.11187, 2019. [Online]. Available: http://arxiv.org/abs/1912.11187
Original
1912
Earlier work this paper cites.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konecný, A. Korolova, F. Koushanfar, S. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao, “Advances and open problems in federated learning,” CoRR , vol. abs/1912.04977, 2019. [Online]. Available: http://arxiv.org/abs/1912.04977
Original
1912
Earlier work this paper cites.
P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in International Conference on the Theory and Applications of Cryptographic Techniques . Springer, 1999, pp. 223–238
1999
Earlier work this paper cites.
G. Liang and S. S. Chawathe, “Privacy-preserving inter-database operations,” in International Conference on Intelligence and Security Informatics . Springer, 2004, pp. 66–82
2004
Earlier work this paper cites.
J. Vaidya and C. Clifton, “Privacy-preserving decision trees over vertically partitioned data,” in IFIP Annual Conference on Data and Applications Security and Privacy . Springer, 2005, pp. 139–152
2005
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International Conference on Theory and Applications of Models of Computation . Springer, 2008, pp. 1–19
2008
Earlier work this paper cites.
J. Vaidya, “A survey of privacy-preserving methods across vertically partitioned data,” in Privacy-preserving data mining . Springer, 2008, pp. 337–358
2008
Earlier work this paper cites.
J. Vaidya, C. Clifton, M. Kantarcioglu, and A. S. Patterson, “Privacy-preserving decision trees over vertically partitioned data,” ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 2, no. 3, p. 14, 2008
2008
Earlier work this paper cites.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
V. Mayer-Schonberger and Y. Padova, “Regime change: Enabling big data through europe’s new data protection regulation,” Colum. Sci. & Tech. L. Rev. , vol. 17, p. 315, 2015
2015
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . ACM, 2015, pp. 1310–1321
2015
Earlier work this paper cites.
R. Bost, R. A. Popa, S. Tu, and S. Goldwasser, “Machine learning classification over encrypted data,” in 22nd Annual Network and Distributed System Security Symposium, NDSS , 2015
2015
Earlier work this paper cites.

Similar

P. Regulation, “The general data protection regulation,” European Commission. Available at: https://eur-lex. europa. eu/legal-content/EN/TXT , 2016
2016
Cited alongside, same era.
J. P. Albrecht, “How the gdpr will change the world,” Eur. Data Prot. L. Rev. , vol. 2, p. 287, 2016
2016
Cited alongside, same era.
B. Goodman and S. Flaxman, “European union regulations on algorithmic decision-making and a” right to explanation”,” arXiv preprint arXiv:1606.08813 , 2016
Original
2016
Cited alongside, same era.
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016
Original
2016
Cited alongside, same era.
KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York, NY, USA: Association for Computing Machinery, 2016

Then

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127 , 2018
Original
2018
Later among the works it cites.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology , vol. 10, no. 2, pp. 12:1–12:19, 2019
2019
Closest in time.
Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, and H. Yu, “Federated learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning , vol. 13, no. 3, pp. 1–207, 2019
2019
Closest in time.
M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning . PMLR, 2019, pp. 4615–4625
2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2016

Cited alongside, same era.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining . ACM, 2016, pp. 785–794

2016

Cited alongside, same era.

S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, G. Smith, and B. Thorne, “Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption,” arXiv preprint arXiv:1711.10677 , 2017

Original

2017

Cited alongside, same era.

P. Mohassel and Y. Zhang, “Secureml: A system for scalable privacy-preserving machine learning,” in 2017 38th IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 19–38

2017

Cited alongside, same era.

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in CCS , 2017, pp. 1175–1191

2017

Cited alongside, same era.

M. Djatmiko, S. Hardy, W. Henecka, H. Ivey-Law, M. Ott, G. Patrini, G. Smith, B. Thorne, and D. Wu, “Privacy-preserving entity resolution and logistic regression on encrypted data,” Private and Secure Machine Learning (PSML) , 2017

2017

Cited alongside, same era.

F. Baldimtsi, D. Papadopoulos, S. Papadopoulos, A. Scafuro, and N. Triandopoulos, “Server-aided secure computation with off-line parties,” in Computer Security - ESORICS 2017 - 22nd European Symposium on Research in Computer Security, Proceedings, Part I , 2017, pp. 103–123

2017

Cited alongside, same era.

M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni, “Bayesian nonparametric federated learning of neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 7252–7261

2019

Closest in time.

Q. Li, Z. Wen, and B. He, “Practical federated gradient boosting decision trees,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 4642–4649

2020

Closest in time.

F. Hanzely, S. Hanzely, S. Horváth, and P. Richtárik, “Lower bounds and optimal algorithms for personalized federated learning,” arXiv preprint arXiv:2010.02372 , 2020

Original

2020

Closest in time.

H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni, “Federated learning with matched averaging,” arXiv preprint arXiv:2002.06440 , 2020

Original

2020

Closest in time.

Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang, “A secure federated transfer learning framework,” IEEE Intelligent Systems , vol. 35, no. 4, pp. 70–82, 2020

2020

Closest in time.

X. Liang, Y. Liu, J. Luo, Y. He, T. Chen, and Q. Yang, “Self-supervised cross-silo federated neural architecture search,” 2021

2021

Closest in time.

L. Zhao, L. Ni, S. Hu, Y. Chen, P. Zhou, F. Xiao, and L. Wu, “Inprivate digging: Enabling tree-based distributed data mining with differential privacy,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications . IEEE, 2018, pp. 2087–2095

2095

Closest in time.