{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:46:28Z","timestamp":1780638388377,"version":"3.54.1"},"reference-count":86,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:p>\n            The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter server. However, existing SV calculation methods for FL assume that the server can access the raw FL models and public test data. This may not be a valid assumption in practice considering the emerging privacy attacks on FL models and the fact that test data might be clients' private assets. Hence, we investigate the problem of\n            <jats:italic>secure SV calculation<\/jats:italic>\n            for cross-silo FL. We first propose\n            <jats:italic>HESV<\/jats:italic>\n            , a one-server solution based solely on homomorphic encryption (HE) for privacy protection, which has limitations in efficiency. To overcome these limitations, we propose\n            <jats:italic>SecSV<\/jats:italic>\n            , an efficient two-server protocol with the following novel features. First, SecSV utilizes a hybrid privacy protection scheme to avoid ciphertext-ciphertext multiplications between test data and models, which are extremely expensive under HE. Second, an efficient secure matrix multiplication method is proposed for SecSV. Third, SecSV strategically identifies and skips some test samples without significantly affecting the evaluation accuracy. Our experiments demonstrate that SecSV is 7.2--36.6\u00d7 as fast as HESV, with a limited loss in the accuracy of calculated SVs.\n          <\/jats:p>","DOI":"10.14778\/3587136.3587141","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T23:11:35Z","timestamp":1683587495000},"page":"1657-1670","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Secure Shapley Value for Cross-Silo Federated Learning"],"prefix":"10.14778","volume":"16","author":[{"given":"Shuyuan","family":"Zheng","sequence":"first","affiliation":[{"name":"Kyoto University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Hokkaido University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatoshi","family":"Yoshikawa","sequence":"additional","affiliation":[{"name":"Kyoto University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. 1253--1269","author":"Bell James Henry","year":"2020","unstructured":"James Henry Bell , Kallista A Bonawitz , Adri\u00e0 Gasc\u00f3n , Tancr\u00e8de Lepoint , and Mariana Raykova . 2020 . Secure single-server aggregation with (poly) logarithmic overhead . In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. 1253--1269 . James Henry Bell, Kallista A Bonawitz, Adri\u00e0 Gasc\u00f3n, Tancr\u00e8de Lepoint, and Mariana Raykova. 2020. Secure single-server aggregation with (poly) logarithmic overhead. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. 1253--1269."},{"key":"e_1_2_1_3_1","volume-title":"TenSEAL: A library for encrypted tensor operations using homomorphic encryption. arXiv preprint arXiv:2104.03152","author":"Benaissa Ayoub","year":"2021","unstructured":"Ayoub Benaissa , Bilal Retiat , Bogdan Cebere , and Alaa Eddine Belfedhal . 2021. TenSEAL: A library for encrypted tensor operations using homomorphic encryption. arXiv preprint arXiv:2104.03152 ( 2021 ). Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, and Alaa Eddine Belfedhal. 2021. TenSEAL: A library for encrypted tensor operations using homomorphic encryption. arXiv preprint arXiv:2104.03152 (2021)."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1175--1191","author":"Bonawitz Keith","year":"2017","unstructured":"Keith Bonawitz , Vladimir Ivanov , Ben Kreuter , Antonio Marcedone , H. Brendan McMahan , Sarvar Patel , Daniel Ramage , Aaron Segal , and Karn Seth . 2017 . Practical Secure Aggregation for Privacy-Preserving Machine Learning . In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1175--1191 . Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1175--1191."},{"key":"e_1_2_1_5_1","first-page":"409","article-title":"Homomorphic Encryption for Arithmetic of Approximate Numbers","volume":"2017","author":"Cheon Jung Hee","year":"2017","unstructured":"Jung Hee Cheon , Andrey Kim , Miran Kim , and Yongsoo Song . 2017 . Homomorphic Encryption for Arithmetic of Approximate Numbers . In Advances in Cryptology - ASIACRYPT 2017. 409 -- 437 . Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. 2017. Homomorphic Encryption for Arithmetic of Approximate Numbers. In Advances in Cryptology - ASIACRYPT 2017. 409--437.","journal-title":"Advances in Cryptology - ASIACRYPT"},{"key":"e_1_2_1_6_1","volume-title":"Communication-computation efficient secure aggregation for federated learning. arXiv preprint arXiv:2012.05433","author":"Choi Beongjun","year":"2020","unstructured":"Beongjun Choi , Jy-yong Sohn, Dong-Jun Han , and Jaekyun Moon . 2020. Communication-computation efficient secure aggregation for federated learning. arXiv preprint arXiv:2012.05433 ( 2020 ). Beongjun Choi, Jy-yong Sohn, Dong-Jun Han, and Jaekyun Moon. 2020. Communication-computation efficient secure aggregation for federated learning. arXiv preprint arXiv:2012.05433 (2020)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. In Theory of Cryptography. 265--284.  Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. In Theory of Cryptography. 265--284.","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Alexandre Evfimievski Johannes Gehrke and Ramakrishnan Srikant. 2003. Limiting privacy breaches in privacy preserving data mining. In ACM SIGMOD. 211--222.  Alexandre Evfimievski Johannes Gehrke and Ramakrishnan Srikant. 2003. Limiting privacy breaches in privacy preserving data mining. In ACM SIGMOD. 211--222.","DOI":"10.1145\/773153.773174"},{"key":"e_1_2_1_9_1","volume-title":"Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948","author":"Fallah Alireza","year":"2020","unstructured":"Alireza Fallah , Aryan Mokhtari , and Asuman Ozdaglar . 2020. Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948 ( 2020 ). Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948 (2020)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1016\/j.artint.2008.05.003","article-title":"A linear approximation method for the Shapley value","volume":"172","author":"Fatima Shaheen S.","year":"2008","unstructured":"Shaheen S. Fatima , Michael Wooldridge , and Nicholas R. Jennings . 2008 . A linear approximation method for the Shapley value . Artificial Intelligence 172 , 14 (2008), 1673 -- 1699 . Shaheen S. Fatima, Michael Wooldridge, and Nicholas R. Jennings. 2008. A linear approximation method for the Shapley value. Artificial Intelligence 172, 14 (2008), 1673--1699.","journal-title":"Artificial Intelligence"},{"key":"e_1_2_1_11_1","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning","volume":"33","author":"Geiping Jonas","year":"2020","unstructured":"Jonas Geiping , Hartmut Bauermeister , Hannah Dr\u00f6ge , and Michael Moeller . 2020 . Inverting gradients-how easy is it to break privacy in federated learning ? Advances in Neural Information Processing Systems 33 (2020), 16937 -- 16947 . Jonas Geiping, Hartmut Bauermeister, Hannah Dr\u00f6ge, and Michael Moeller. 2020. Inverting gradients-how easy is it to break privacy in federated learning? Advances in Neural Information Processing Systems 33 (2020), 16937--16947.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_12_1","volume-title":"Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer , Tassilo Klein , and Moin Nabi . 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 ( 2017 ). Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017)."},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning","volume":"97","author":"Ghorbani Amirata","year":"2019","unstructured":"Amirata Ghorbani and James Zou . 2019 . Data shapley: Equitable valuation of data for machine learning . In Proceedings of the 36th International Conference on Machine Learning , Vol. 97 . 2242--2251. Amirata Ghorbani and James Zou. 2019. Data shapley: Equitable valuation of data for machine learning. In Proceedings of the 36th International Conference on Machine Learning, Vol. 97. 2242--2251."},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of The 33rd International Conference on Machine Learning. 201--210","author":"Gilad-Bachrach Ran","year":"2016","unstructured":"Ran Gilad-Bachrach , Nathan Dowlin , Kim Laine , Kristin Lauter , Michael Naehrig , and John Wernsing . 2016 . CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy . In Proceedings of The 33rd International Conference on Machine Learning. 201--210 . Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016. CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. In Proceedings of The 33rd International Conference on Machine Learning. 201--210."},{"key":"e_1_2_1_15_1","volume-title":"Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh.","author":"Girgis Antonious M","year":"2021","unstructured":"Antonious M Girgis , Deepesh Data , Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh. 2021 . Shuffled model of federated learning: Privacy, accuracy and communication trade-offs. IEEE journal on selected areas in information theory 2, 1 (2021), 464--478. Antonious M Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh. 2021. Shuffled model of federated learning: Privacy, accuracy and communication trade-offs. IEEE journal on selected areas in information theory 2, 1 (2021), 464--478."},{"key":"e_1_2_1_16_1","first-page":"1736","article-title":"V eri fl: Communication-efficient and fast verifiable aggregation for federated learning","volume":"16","author":"Guo Xiaojie","year":"2020","unstructured":"Xiaojie Guo , Zheli Liu , Jin Li , Jiqiang Gao , Boyu Hou , Changyu Dong , and Thar Baker . 2020 . V eri fl: Communication-efficient and fast verifiable aggregation for federated learning . IEEE Transactions on Information Forensics and Security 16 (2020), 1736 -- 1751 . Xiaojie Guo, Zheli Liu, Jin Li, Jiqiang Gao, Boyu Hou, Changyu Dong, and Thar Baker. 2020. V eri fl: Communication-efficient and fast verifiable aggregation for federated learning. IEEE Transactions on Information Forensics and Security 16 (2020), 1736--1751.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_2_1_17_1","volume-title":"Replication-robust payoff-allocation for machine learning data markets. arXiv preprint arXiv:2006.14583","author":"Han Dongge","year":"2020","unstructured":"Dongge Han , Michael Wooldridge , Alex Rogers , Shruti Tople , Olga Ohrimenko , and Sebastian Tschiatschek . 2020. Replication-robust payoff-allocation for machine learning data markets. arXiv preprint arXiv:2006.14583 ( 2020 ). Dongge Han, Michael Wooldridge, Alex Rogers, Shruti Tople, Olga Ohrimenko, and Sebastian Tschiatschek. 2020. Replication-robust payoff-allocation for machine learning data markets. arXiv preprint arXiv:2006.14583 (2020)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1515\/popets-2018-0024","article-title":"Privacy-preserving Machine Learning as a Service","volume":"2018","author":"Hesamifard Ehsan","year":"2018","unstructured":"Ehsan Hesamifard , Hassan Takabi , Mehdi Ghasemi , and Rebecca N Wright . 2018 . Privacy-preserving Machine Learning as a Service . Proc. Priv. Enhancing Technol. 2018 , 3 (2018), 123 -- 142 . Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi, and Rebecca N Wright. 2018. Privacy-preserving Machine Learning as a Service. Proc. Priv. Enhancing Technol. 2018, 3 (2018), 123--142.","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3523273","article-title":"Membership inference attacks on machine learning: A survey","volume":"54","author":"Hu Hongsheng","year":"2022","unstructured":"Hongsheng Hu , Zoran Salcic , Lichao Sun , Gillian Dobbie , Philip S Yu , and Xuyun Zhang . 2022 . Membership inference attacks on machine learning: A survey . ACM Computing Surveys (CSUR) 54 , 11s (2022), 1 -- 37 . Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S Yu, and Xuyun Zhang. 2022. Membership inference attacks on machine learning: A survey. ACM Computing Surveys (CSUR) 54, 11s (2022), 1--37.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JIOT.2020.2991416","article-title":"Personalized federated learning with differential privacy","volume":"7","author":"Hu Rui","year":"2020","unstructured":"Rui Hu , Yuanxiong Guo , Hongning Li , Qingqi Pei , and Yanmin Gong . 2020 . Personalized federated learning with differential privacy . IEEE Internet of Things Journal 7 , 10 (2020), 9530 -- 9539 . Rui Hu, Yuanxiong Guo, Hongning Li, Qingqi Pei, and Yanmin Gong. 2020. Personalized federated learning with differential privacy. IEEE Internet of Things Journal 7, 10 (2020), 9530--9539.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Huang Yutao","year":"2021","unstructured":"Yutao Huang , Lingyang Chu , Zirui Zhou , Lanjun Wang , Jiangchuan Liu , Jian Pei , and Yong Zhang . 2021 . Personalized cross-silo federated learning on non-iid data . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35 . 7865--7873. Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang. 2021. Personalized cross-silo federated learning on non-iid data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 7865--7873."},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.14778\/3342263.3342637","article-title":"Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms","volume":"12","author":"Jia Ruoxi","year":"2019","unstructured":"Ruoxi Jia , David Dao , Boxin Wang , Frances Ann Hubis , Nezihe Merve Gurel , Bo Li4 Ce Zhang , and Costas Spanos1 Dawn Song . 2019 . Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms . Proceedings of the VLDB Endowment 12 , 11 (2019), 1610 -- 1623 . Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li4 Ce Zhang, and Costas Spanos1 Dawn Song. 2019. Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms. Proceedings of the VLDB Endowment 12, 11 (2019), 1610--1623.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_23_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. 1167--1176","author":"Jia Ruoxi","year":"2019","unstructured":"Ruoxi Jia , David Dao , Boxin Wang , Frances Ann Hubis , Nick Hynes , Nezihe Merve G\u00fcrel , Bo Li , Ce Zhang , Dawn Song , and Costas J Spanos . 2019 . Towards efficient data valuation based on the shapley value . In The 22nd International Conference on Artificial Intelligence and Statistics. 1167--1176 . Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve G\u00fcrel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos. 2019. Towards efficient data valuation based on the shapley value. In The 22nd International Conference on Artificial Intelligence and Statistics. 1167--1176."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 1209--1222","author":"Jiang Xiaoqian","year":"2018","unstructured":"Xiaoqian Jiang , Miran Kim , Kristin Lauter , and Yongsoo Song . 2018 . Secure Outsourced Matrix Computation and Application to Neural Networks . In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 1209--1222 . Xiaoqian Jiang, Miran Kim, Kristin Lauter, and Yongsoo Song. 2018. Secure Outsourced Matrix Computation and Application to Neural Networks. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 1209--1222."},{"key":"e_1_2_1_25_1","volume-title":"FLASHE: Additively symmetric homomorphic encryption for cross-silo federated learning. arXiv preprint arXiv:2109.00675","author":"Jiang Zhifeng","year":"2021","unstructured":"Zhifeng Jiang , Wei Wang , and Yang Liu . 2021 . FLASHE: Additively symmetric homomorphic encryption for cross-silo federated learning. arXiv preprint arXiv:2109.00675 (2021). Zhifeng Jiang, Wei Wang, and Yang Liu. 2021. FLASHE: Additively symmetric homomorphic encryption for cross-silo federated learning. arXiv preprint arXiv:2109.00675 (2021)."},{"key":"e_1_2_1_26_1","volume-title":"2019 IEEE European Symposium on Security and Privacy (EuroS&P). 512--527","author":"Juuti Mika","year":"2019","unstructured":"Mika Juuti , Sebastian Szyller , Samuel Marchal , and N Asokan . 2019 . PRADA: protecting against DNN model stealing attacks . In 2019 IEEE European Symposium on Security and Privacy (EuroS&P). 512--527 . Mika Juuti, Sebastian Szyller, Samuel Marchal, and N Asokan. 2019. PRADA: protecting against DNN model stealing attacks. In 2019 IEEE European Symposium on Security and Privacy (EuroS&P). 512--527."},{"key":"e_1_2_1_27_1","volume-title":"GAZELLE: A Low Latency Framework for Secure Neural Network Inference. In 27th USENIX Security Symposium (USENIX Security 18)","author":"Juvekar Chiraag","year":"2018","unstructured":"Chiraag Juvekar , Vinod Vaikuntanathan , and Anantha Chandrakasan . 2018 . GAZELLE: A Low Latency Framework for Secure Neural Network Inference. In 27th USENIX Security Symposium (USENIX Security 18) . 1651--1669. Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. 2018. GAZELLE: A Low Latency Framework for Secure Neural Network Inference. In 27th USENIX Security Symposium (USENIX Security 18). 1651--1669."},{"key":"e_1_2_1_28_1","volume-title":"FASTSECAGG: Scalable secure aggregation for privacy-preserving federated learning. arXiv preprint arXiv:2009.11248","author":"Kadhe Swanand","year":"2020","unstructured":"Swanand Kadhe , Nived Rajaraman , O Ozan Koyluoglu , and Kannan Ramchandran . 2020 . FASTSECAGG: Scalable secure aggregation for privacy-preserving federated learning. arXiv preprint arXiv:2009.11248 (2020). Swanand Kadhe, Nived Rajaraman, O Ozan Koyluoglu, and Kannan Ramchandran. 2020. FASTSECAGG: Scalable secure aggregation for privacy-preserving federated learning. arXiv preprint arXiv:2009.11248 (2020)."},{"key":"e_1_2_1_29_1","volume-title":"International Conference on Machine Learning. 5201--5212","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz , Ziyu Liu , and Thomas Steinke . 2021 . The distributed discrete gaussian mechanism for federated learning with secure aggregation . In International Conference on Machine Learning. 5201--5212 . Peter Kairouz, Ziyu Liu, and Thomas Steinke. 2021. The distributed discrete gaussian mechanism for federated learning with secure aggregation. In International Conference on Machine Learning. 5201--5212."},{"key":"e_1_2_1_30_1","volume-title":"Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz , H Brendan McMahan , Brendan Avent , Aur\u00e9lien Bellet , Mehdi Bennis , Arjun Nitin Bhagoji , Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021 . Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14, 1--2 (2021), 1--210. Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14, 1--2 (2021), 1--210."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun Yann","year":"1998","unstructured":"Yann LeCun , L\u00e9on Bottou , Yoshua Bengio , and Patrick Haffner . 1998 . Gradient-based learning applied to document recognition . Proc. IEEE 86 , 11 (1998), 2278 -- 2324 . Yann LeCun, L\u00e9on Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324.","journal-title":"Proc. IEEE"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 7th ACM international conference on bioinformatics, computational biology, and health informatics. 434--442","author":"Lee Byunghan","year":"2016","unstructured":"Byunghan Lee , Junghwan Baek , Seunghyun Park , and Sungroh Yoon . 2016 . deepTarget: End-to-end learning framework for microRNA target prediction using deep recurrent neural networks . In Proceedings of the 7th ACM international conference on bioinformatics, computational biology, and health informatics. 434--442 . Byunghan Lee, Junghwan Baek, Seunghyun Park, and Sungroh Yoon. 2016. deepTarget: End-to-end learning framework for microRNA target prediction using deep recurrent neural networks. In Proceedings of the 7th ACM international conference on bioinformatics, computational biology, and health informatics. 434--442."},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li Tian","year":"2020","unstructured":"Tian Li , Anit Kumar Sahu , Ameet Talwalkar , and Virginia Smith . 2020 . Federated learning: Challenges, methods, and future directions . IEEE Signal Processing Magazine 37 , 3 (2020), 50 -- 60 . Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50--60.","journal-title":"IEEE Signal Processing Magazine"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","first-page":"957","DOI":"10.14778\/3447689.3447700","article-title":"Dealer: An end-to-end model marketplace with differential privacy","volume":"14","author":"Liu Jinfei","year":"2021","unstructured":"Jinfei Liu , Jian Lou , Junxu Liu , Li Xiong , Jian Pei , and Jimeng Sun . 2021 . Dealer: An end-to-end model marketplace with differential privacy . Proceedings of the VLDB Endowment 14 , 6 (2021), 957 -- 969 . Jinfei Liu, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, and Jimeng Sun. 2021. Dealer: An end-to-end model marketplace with differential privacy. Proceedings of the VLDB Endowment 14, 6 (2021), 957--969.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Liu Ruixuan","year":"2021","unstructured":"Ruixuan Liu , Yang Cao , Hong Chen , Ruoyang Guo , and Masatoshi Yoshikawa . 2021 . FLAME: Differentially private federated learning in the shuffle model . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35 . 8688--8696. Ruixuan Liu, Yang Cao, Hong Chen, Ruoyang Guo, and Masatoshi Yoshikawa. 2021. FLAME: Differentially private federated learning in the shuffle model. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 8688--8696."},{"key":"e_1_2_1_36_1","volume-title":"FedCoin: A Peer-to-Peer Payment System for Federated Learning","author":"Liu Yuan","unstructured":"Yuan Liu , Zhengpeng Ai , Shuai Sun , Shuangfeng Zhang , Zelei Liu , and Han Yu. 2020. FedCoin: A Peer-to-Peer Payment System for Federated Learning . Springer International Publishing , 125--138. Yuan Liu, Zhengpeng Ai, Shuai Sun, Shuangfeng Zhang, Zelei Liu, and Han Yu. 2020. FedCoin: A Peer-to-Peer Payment System for Federated Learning. Springer International Publishing, 125--138."},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/MIS.2020.2988525","article-title":"A secure federated transfer learning framework","volume":"35","author":"Liu Yang","year":"2020","unstructured":"Yang Liu , Yan Kang , Chaoping Xing , Tianjian Chen , and Qiang Yang . 2020 . A secure federated transfer learning framework . IEEE Intelligent Systems 35 , 4 (2020), 70 -- 82 . Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, and Qiang Yang. 2020. A secure federated transfer learning framework. IEEE Intelligent Systems 35, 4 (2020), 70--82.","journal-title":"IEEE Intelligent Systems"},{"key":"e_1_2_1_38_1","first-page":"1","article-title":"GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning","volume":"13","author":"Liu Zelei","year":"2022","unstructured":"Zelei Liu , Yuanyuan Chen , Han Yu , Yang Liu , and Lizhen Cui . 2022 . GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning . ACM Transactions on Intelligent Systems and Technology (TIST) 13 , 4 (2022), 1 -- 21 . Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, and Lizhen Cui. 2022. GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning. ACM Transactions on Intelligent Systems and Technology (TIST) 13, 4 (2022), 1--21.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee . 2017 . A Unified Approach to Interpreting Model Predictions . In Advances in Neural Information Processing Systems , Vol. 30 . Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems, Vol. 30."},{"key":"e_1_2_1_40_1","volume-title":"Yu","author":"Lyu Lingjuan","year":"2022","unstructured":"Lingjuan Lyu , Han Yu , Xingjun Ma , Chen Chen , Lichao Sun , Jun Zhao , Qiang Yang , and Philip S . Yu . 2022 . Privacy and Robustness in Federated Learning : Attacks and Defenses. IEEE Transactions on Neural Networks and Learning Systems ( 2022). Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S. Yu. 2022. Privacy and Robustness in Federated Learning: Attacks and Defenses. IEEE Transactions on Neural Networks and Learning Systems (2022)."},{"key":"e_1_2_1_41_1","volume-title":"International Journal of Intelligent Systems","author":"Ma Jing","year":"2022","unstructured":"Jing Ma , Si-Ahmed Naas , Stephan Sigg , and Xixiang Lyu . 2022. Privacy-preserving federated learning based on multi-key homomorphic encryption . International Journal of Intelligent Systems ( 2022 ). Jing Ma, Si-Ahmed Naas, Stephan Sigg, and Xixiang Lyu. 2022. Privacy-preserving federated learning based on multi-key homomorphic encryption. International Journal of Intelligent Systems (2022)."},{"key":"e_1_2_1_42_1","volume-title":"2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW). IEEE, 88--91","author":"Ma Shuaicheng","year":"2021","unstructured":"Shuaicheng Ma , Yang Cao , and Li Xiong . 2021 . Transparent contribution evaluation for secure federated learning on blockchain . In 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW). IEEE, 88--91 . Shuaicheng Ma, Yang Cao, and Li Xiong. 2021. Transparent contribution evaluation for secure federated learning on blockchain. In 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW). IEEE, 88--91."},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 1273--1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , and Blaise Ag\u00fcera y Arcas . 2017 . Communication-Efficient Learning of Deep Networks from Decentralized Data . In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 1273--1282 . Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 1273--1282."},{"key":"e_1_2_1_45_1","volume-title":"Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963","author":"McMahan H Brendan","year":"2017","unstructured":"H Brendan McMahan , Daniel Ramage , Kunal Talwar , and Li Zhang . 2017. Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963 ( 2017 ). H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017. Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963 (2017)."},{"key":"e_1_2_1_46_1","doi-asserted-by":"crossref","first-page":"3740","DOI":"10.1109\/TIFS.2021.3090959","article-title":"DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption","volume":"16","author":"Meftah Souhail","year":"2021","unstructured":"Souhail Meftah , Benjamin Hong Meng Tan , Chan Fook Mun , Khin Mi Mi Aung , Bharadwaj Veeravalli , and Vijay Chandrasekhar . 2021 . DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption . IEEE Transactions on Information Forensics and Security 16 (2021), 3740 -- 3752 . Souhail Meftah, Benjamin Hong Meng Tan, Chan Fook Mun, Khin Mi Mi Aung, Bharadwaj Veeravalli, and Vijay Chandrasekhar. 2021. DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 16 (2021), 3740--3752.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_2_1_47_1","volume-title":"mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology 15, 10","author":"Menor Mark","year":"2014","unstructured":"Mark Menor , Travers Ching , Xun Zhu , David Garmire , and Lana X Garmire . 2014. mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology 15, 10 ( 2014 ), 1--16. Mark Menor, Travers Ching, Xun Zhu, David Garmire, and Lana X Garmire. 2014. mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology 15, 10 (2014), 1--16."},{"key":"e_1_2_1_48_1","volume-title":"Delphi: A Cryptographic Inference Service for Neural Networks. In 29th USENIX Security Symposium (USENIX Security 20)","author":"Mishra Pratyush","year":"2020","unstructured":"Pratyush Mishra , Ryan Lehmkuhl , Akshayaram Srinivasan , Wenting Zheng , and Raluca Ada Popa . 2020 . Delphi: A Cryptographic Inference Service for Neural Networks. In 29th USENIX Security Symposium (USENIX Security 20) . 2505--2522. Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. 2020. Delphi: A Cryptographic Inference Service for Neural Networks. In 29th USENIX Security Symposium (USENIX Security 20). 2505--2522."},{"key":"e_1_2_1_49_1","volume-title":"2017 IEEE symposium on security and privacy (SP). IEEE, 19--38","author":"Mohassel Payman","year":"2017","unstructured":"Payman Mohassel and Yupeng Zhang . 2017 . SecureML: A system for scalable privacy-preserving machine learning . In 2017 IEEE symposium on security and privacy (SP). IEEE, 19--38 . Payman Mohassel and Yupeng Zhang. 2017. SecureML: A system for scalable privacy-preserving machine learning. In 2017 IEEE symposium on security and privacy (SP). IEEE, 19--38."},{"key":"e_1_2_1_50_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","article-title":"A data-driven approach to predict the success of bank telemarketing","volume":"62","author":"Moro S\u00e9rgio","year":"2014","unstructured":"S\u00e9rgio Moro , Paulo Cortez , and Paulo Rita . 2014 . A data-driven approach to predict the success of bank telemarketing . Decision Support Systems 62 (2014), 22 -- 31 . S\u00e9rgio Moro, Paulo Cortez, and Paulo Rita. 2014. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems 62 (2014), 22--31.","journal-title":"Decision Support Systems"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence 35","author":"Nagalapatti Lokesh","year":"2021","unstructured":"Lokesh Nagalapatti and Ramasuri Narayanam . 2021 . Game of Gradients: Mitigating Irrelevant Clients in Federated Learning . Proceedings of the AAAI Conference on Artificial Intelligence 35 , 10 (2021), 9046--9054. Lokesh Nagalapatti and Ramasuri Narayanam. 2021. Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 10 (2021), 9046--9054."},{"key":"e_1_2_1_52_1","volume-title":"International Conference on Artificial Intelligence and Statistics. 10110--10145","author":"Noble Maxence","year":"2022","unstructured":"Maxence Noble , Aur\u00e9lien Bellet , and Aymeric Dieuleveut . 2022 . Differentially private federated learning on heterogeneous data . In International Conference on Artificial Intelligence and Statistics. 10110--10145 . Maxence Noble, Aur\u00e9lien Bellet, and Aymeric Dieuleveut. 2022. Differentially private federated learning on heterogeneous data. In International Conference on Artificial Intelligence and Statistics. 10110--10145."},{"key":"e_1_2_1_53_1","volume-title":"Collaborative machine learning markets with data-replication-robust payments. arXiv preprint arXiv:1911.09052","author":"Ohrimenko Olga","year":"2019","unstructured":"Olga Ohrimenko , Shruti Tople , and Sebastian Tschiatschek . 2019. Collaborative machine learning markets with data-replication-robust payments. arXiv preprint arXiv:1911.09052 ( 2019 ). Olga Ohrimenko, Shruti Tople, and Sebastian Tschiatschek. 2019. Collaborative machine learning markets with data-replication-robust payments. arXiv preprint arXiv:1911.09052 (2019)."},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 4954--4963","author":"Orekondy Tribhuvanesh","year":"2019","unstructured":"Tribhuvanesh Orekondy , Bernt Schiele , and Mario Fritz . 2019 . Knockoff Nets: Stealing functionality of black-box models . In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 4954--4963 . Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. 2019. Knockoff Nets: Stealing functionality of black-box models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 4954--4963."},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the 2017 ACM on Asia conference on computer and communications security. 506--519","author":"Papernot Nicolas","year":"2017","unstructured":"Nicolas Papernot , Patrick McDaniel , Ian Goodfellow , Somesh Jha , Z Berkay Celik , and Ananthram Swami . 2017 . Practical black-box attacks against machine learning . In Proceedings of the 2017 ACM on Asia conference on computer and communications security. 506--519 . Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017. Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security. 506--519."},{"key":"e_1_2_1_56_1","unstructured":"Matthias Paulik et al. 2021. Federated evaluation and tuning for on-device personalization: System design & applications. arXiv:2102.08503 (2021).  Matthias Paulik et al. 2021. Federated evaluation and tuning for on-device personalization: System design & applications. arXiv:2102.08503 (2021)."},{"key":"e_1_2_1_57_1","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/TIFS.2017.2787987","article-title":"Privacy-Preserving Deep Learning via Additively Homomorphic Encryption","volume":"13","author":"Phong Le Trieu","year":"2018","unstructured":"Le Trieu Phong , Yoshinori Aono , Takuya Hayashi , Lihua Wang , and Shiho Moriai . 2018 . Privacy-Preserving Deep Learning via Additively Homomorphic Encryption . IEEE Transactions on Information Forensics and Security 13 , 5 (2018), 1333 -- 1345 . Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai. 2018. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 13, 5 (2018), 1333--1345.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_2_1_58_1","volume-title":"Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). 26--39","author":"Reagen Brandon","year":"2021","unstructured":"Brandon Reagen , Woo-Seok Choi , Yeongil Ko , Vincent T. Lee , Hsien-Hsin S. Lee , Gu-Yeon Wei , and David Brooks . 2021 . Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). 26--39 . Brandon Reagen, Woo-Seok Choi, Yeongil Ko, Vincent T. Lee, Hsien-Hsin S. Lee, Gu-Yeon Wei, and David Brooks. 2021. Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). 26--39."},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the 2018 on Asia Conference on Computer and Communications Security. 707--721","author":"Riazi M Sa","year":"2018","unstructured":"M Sa degh Riazi , Christian Weinert , Oleksandr Tkachenko , Ebrahim M Songhori , Thomas Schneider , and Farinaz Koushanfar . 2018 . Chameleon: A hybrid secure computation framework for machine learning applications . In Proceedings of the 2018 on Asia Conference on Computer and Communications Security. 707--721 . M Sa degh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim M Songhori, Thomas Schneider, and Farinaz Koushanfar. 2018. Chameleon: A hybrid secure computation framework for machine learning applications. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security. 707--721."},{"key":"e_1_2_1_60_1","volume-title":"POSEIDON: Privacy-Preserving Federated Neural Network Learning. In 28th Annual Network and Distributed System Security Symposium, NDSS 2021","author":"Sav Sinem","year":"2021","unstructured":"Sinem Sav , Apostolos Pyrgelis , Juan Ram\u00f3n Troncoso-Pastoriza , David Froelicher , Jean-Philippe Bossuat , Joao Sa Sousa , and Jean-Pierre Hubaux . 2021 . POSEIDON: Privacy-Preserving Federated Neural Network Learning. In 28th Annual Network and Distributed System Security Symposium, NDSS 2021 , virtually, February 21 --25 , 2021. Sinem Sav, Apostolos Pyrgelis, Juan Ram\u00f3n Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, Joao Sa Sousa, and Jean-Pierre Hubaux. 2021. POSEIDON: Privacy-Preserving Federated Neural Network Learning. In 28th Annual Network and Distributed System Security Symposium, NDSS 2021, virtually, February 21--25, 2021."},{"key":"e_1_2_1_61_1","first-page":"11","article-title":"How to Share a","volume":"22","author":"Shamir Adi","year":"1979","unstructured":"Adi Shamir . 1979 . How to Share a Secret. Commun. ACM 22 , 11 (nov 1979), 612--613. Adi Shamir. 1979. How to Share a Secret. Commun. ACM 22, 11 (nov 1979), 612--613.","journal-title":"Secret. Commun. ACM"},{"key":"e_1_2_1_62_1","volume-title":"A Value for n-Person Games","author":"Shapley L. S.","unstructured":"L. S. Shapley . 1953. A Value for n-Person Games . Princeton University Press , 307--318. L. S. Shapley. 1953. A Value for n-Person Games. Princeton University Press, 307--318."},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310--1321","author":"Shokri Reza","year":"2015","unstructured":"Reza Shokri and Vitaly Shmatikov . 2015 . Privacy-preserving deep learning . In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310--1321 . Reza Shokri and Vitaly Shmatikov. 2015. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310--1321."},{"key":"e_1_2_1_64_1","volume-title":"2017 IEEE symposium on security and privacy (SP). IEEE, 3--18","author":"Shokri Reza","year":"2017","unstructured":"Reza Shokri , Marco Stronati , Congzheng Song , and Vitaly Shmatikov . 2017 . Membership inference attacks against machine learning models . In 2017 IEEE symposium on security and privacy (SP). IEEE, 3--18 . Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP). IEEE, 3--18."},{"key":"e_1_2_1_65_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning. 8927--8936","author":"Ling Sim Rachael Hwee","year":"2020","unstructured":"Rachael Hwee Ling Sim , Yehong Zhang , Mun Choon Chan , and Bryan Kian Hsiang Low . 2020 . Collaborative Machine Learning with Incentive-Aware Model Rewards . In Proceedings of the 37th International Conference on Machine Learning. 8927--8936 . Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, and Bryan Kian Hsiang Low. 2020. Collaborative Machine Learning with Incentive-Aware Model Rewards. In Proceedings of the 37th International Conference on Machine Learning. 8927--8936."},{"key":"e_1_2_1_66_1","first-page":"2168","article-title":"Byzantine-resilient secure federated learning","volume":"39","author":"So Jinhyun","year":"2020","unstructured":"Jinhyun So , Ba\u015fak G\u00fcler , and A Salman Avestimehr . 2020 . Byzantine-resilient secure federated learning . IEEE Journal on Selected Areas in Communications 39 , 7 (2020), 2168 -- 2181 . Jinhyun So, Ba\u015fak G\u00fcler, and A Salman Avestimehr. 2020. Byzantine-resilient secure federated learning. IEEE Journal on Selected Areas in Communications 39, 7 (2020), 2168--2181.","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"e_1_2_1_67_1","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/JSAIT.2021.3054610","article-title":"Turbo-Aggregate: Breaking the quadratic aggregation barrier in secure federated learning","volume":"2","author":"So Jinhyun","year":"2021","unstructured":"Jinhyun So , Ba\u015fak G\u00fcler , and A Salman Avestimehr . 2021 . Turbo-Aggregate: Breaking the quadratic aggregation barrier in secure federated learning . IEEE Journal on Selected Areas in Information Theory 2 , 1 (2021), 479 -- 489 . Jinhyun So, Ba\u015fak G\u00fcler, and A Salman Avestimehr. 2021. Turbo-Aggregate: Breaking the quadratic aggregation barrier in secure federated learning. IEEE Journal on Selected Areas in Information Theory 2, 1 (2021), 479--489.","journal-title":"IEEE Journal on Selected Areas in Information Theory"},{"key":"e_1_2_1_68_1","first-page":"694","article-title":"LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning","volume":"4","author":"So Jinhyun","year":"2022","unstructured":"Jinhyun So , Corey J Nolet , Chien-Sheng Yang , Songze Li , Qian Yu , Ramy E Ali , Basak Guler , and Salman Avestimehr . 2022 . LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning . Proceedings of Machine Learning and Systems 4 (2022), 694 -- 720 . Jinhyun So, Corey J Nolet, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E Ali, Basak Guler, and Salman Avestimehr. 2022. LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning. Proceedings of Machine Learning and Systems 4 (2022), 694--720.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_2_1_69_1","volume-title":"Profit Allocation for Federated Learning. In 2019 IEEE International Conference on Big Data (Big Data). 2577--2586","author":"Song Tianshu","year":"2019","unstructured":"Tianshu Song , Yongxin Tong , and Shuyue Wei . 2019 . Profit Allocation for Federated Learning. In 2019 IEEE International Conference on Big Data (Big Data). 2577--2586 . Tianshu Song, Yongxin Tong, and Shuyue Wei. 2019. Profit Allocation for Federated Learning. In 2019 IEEE International Conference on Big Data (Big Data). 2577--2586."},{"key":"e_1_2_1_70_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Tay Sebastian Shenghong","year":"2022","unstructured":"Sebastian Shenghong Tay , Xinyi Xu , Chuan Sheng Foo , and Bryan Kian Hsiang Low . 2022 . Incentivizing collaboration in machine learning via synthetic data rewards . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36 . 9448--9456. Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, and Bryan Kian Hsiang Low. 2022. Incentivizing collaboration in machine learning via synthetic data rewards. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 9448--9456."},{"key":"e_1_2_1_71_1","volume-title":"25th USENIX security symposium (USENIX Security 16). 601--618.","author":"Tram\u00e8r Florian","unstructured":"Florian Tram\u00e8r , Fan Zhang , Ari Juels , Michael K Reiter , and Thomas Ristenpart . 2016. Stealing machine learning models via prediction {APIs} . In 25th USENIX security symposium (USENIX Security 16). 601--618. Florian Tram\u00e8r, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction {APIs}. In 25th USENIX security symposium (USENIX Security 16). 601--618."},{"key":"e_1_2_1_72_1","volume-title":"2019 IEEE International Conference on Big Data (Big Data). IEEE, 2587--2596","author":"Triastcyn Aleksei","year":"2019","unstructured":"Aleksei Triastcyn and Boi Faltings . 2019 . Federated learning with bayesian differential privacy . In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2587--2596 . Aleksei Triastcyn and Boi Faltings. 2019. Federated learning with bayesian differential privacy. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2587--2596."},{"key":"e_1_2_1_73_1","volume-title":"Proceedings of the 12th ACM workshop on artificial intelligence and security. 1--11","author":"Truex Stacey","year":"2019","unstructured":"Stacey Truex , Nathalie Baracaldo , Ali Anwar , Thomas Steinke , Heiko Ludwig , Rui Zhang , and Yi Zhou . 2019 . A hybrid approach to privacy-preserving federated learning . In Proceedings of the 12th ACM workshop on artificial intelligence and security. 1--11 . Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019. A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM workshop on artificial intelligence and security. 1--11."},{"key":"e_1_2_1_74_1","unstructured":"Kangkang Wang et al. 2019. Federated evaluation of on-device personalization. arXiv:1910.10252 (2019).  Kangkang Wang et al. 2019. Federated evaluation of on-device personalization. arXiv:1910.10252 (2019)."},{"key":"e_1_2_1_75_1","volume-title":"Federated Learning","author":"Wang Tianhao","unstructured":"Tianhao Wang , Johannes Rausch , Ce Zhang , Ruoxi Jia , and Dawn Song . 2020. A principled approach to data valuation for federated learning . In Federated Learning . Springer , 153--167. Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, and Dawn Song. 2020. A principled approach to data valuation for federated learning. In Federated Learning. Springer, 153--167."},{"key":"e_1_2_1_76_1","volume-title":"Efficient and Fair Data Valuation for Horizontal Federated Learning","author":"Wei Shuyue","unstructured":"Shuyue Wei , Yongxin Tong , Zimu Zhou , and Tianshu Song . 2020. Efficient and Fair Data Valuation for Horizontal Federated Learning . Springer International Publishing , 139--152. Shuyue Wei, Yongxin Tong, Zimu Zhou, and Tianshu Song. 2020. Efficient and Fair Data Valuation for Horizontal Federated Learning. Springer International Publishing, 139--152."},{"key":"e_1_2_1_77_1","volume-title":"WTDP-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection","author":"Yang Chengyi","year":"2022","unstructured":"Chengyi Yang , Jia Liu , Hao Sun , Tongzhi Li , and Zengxiang Li. 2022. WTDP-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection . IEEE Transactions on Big Data ( 2022 ), 1--10. Chengyi Yang, Jia Liu, Hao Sun, Tongzhi Li, and Zengxiang Li. 2022. WTDP-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection. IEEE Transactions on Big Data (2022), 1--10."},{"key":"e_1_2_1_78_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang Qiang","year":"2019","unstructured":"Qiang Yang , Yang Liu , Tianjian Chen , and Yongxin Tong . 2019 . Federated machine learning: Concept and applications . ACM Transactions on Intelligent Systems and Technology (TIST) 10 , 2 (2019), 1 -- 19 . Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1--19.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_2_1_79_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 16337--16346","author":"Yin Hongxu","year":"2021","unstructured":"Hongxu Yin , Arun Mallya , Arash Vahdat , Jose M Alvarez , Jan Kautz , and Pavlo Molchanov . 2021 . See through gradients: Image batch recovery via gradinversion . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 16337--16346 . Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov. 2021. See through gradients: Image batch recovery via gradinversion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 16337--16346."},{"key":"e_1_2_1_80_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning. 7252--7261","author":"Yurochkin Mikhail","year":"2019","unstructured":"Mikhail Yurochkin , Mayank Agarwal , Soumya Ghosh , Kristjan Greenewald , Nghia Hoang , and Yasaman Khazaeni . 2019 . Bayesian Nonparametric Federated Learning of Neural Networks . In Proceedings of the 36th International Conference on Machine Learning. 7252--7261 . Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni. 2019. Bayesian Nonparametric Federated Learning of Neural Networks. In Proceedings of the 36th International Conference on Machine Learning. 7252--7261."},{"key":"e_1_2_1_81_1","volume-title":"BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20)","author":"Zhang Chengliang","year":"2020","unstructured":"Chengliang Zhang , Suyi Li , Junzhe Xia , Wei Wang , Feng Yan , and Yang Liu . 2020 . BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20) . 493--506. Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020. BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). 493--506."},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3933--3939","author":"Zhang Qiao","unstructured":"Qiao Zhang , Cong Wang , Hongyi Wu , Chunsheng Xin , and Tran V. Phuong . 2018. GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning . In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3933--3939 . Qiao Zhang, Cong Wang, Hongyi Wu, Chunsheng Xin, and Tran V. Phuong. 2018. GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3933--3939."},{"key":"e_1_2_1_83_1","volume-title":"50th International Conference on Parallel Processing. 1--10","author":"Zhang Shulai","year":"2021","unstructured":"Shulai Zhang , Zirui Li , Quan Chen , Wenli Zheng , Jingwen Leng , and Minyi Guo . 2021 . Dubhe: Towards data unbiasedness with homomorphic encryption in federated learning client selection . In 50th International Conference on Parallel Processing. 1--10 . Shulai Zhang, Zirui Li, Quan Chen, Wenli Zheng, Jingwen Leng, and Minyi Guo. 2021. Dubhe: Towards data unbiasedness with homomorphic encryption in federated learning client selection. In 50th International Conference on Parallel Processing. 1--10."},{"key":"e_1_2_1_84_1","volume-title":"Character-level convolutional networks for text classification. Advances in neural information processing systems 28","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang , Junbo Zhao , and Yann LeCun . 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 ( 2015 ). Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_2_1_85_1","volume-title":"Konda Reddy Mopuri, and Hakan Bilen","author":"Zhao Bo","year":"2020","unstructured":"Bo Zhao , Konda Reddy Mopuri, and Hakan Bilen . 2020 . iDLG: Improved de ep leakage from gradients. arXiv preprint arXiv:2001.02610 (2020). Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. iDLG: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610 (2020)."},{"key":"e_1_2_1_86_1","doi-asserted-by":"crossref","first-page":"8836","DOI":"10.1109\/JIOT.2020.3037194","article-title":"Local differential privacy-based federated learning for internet of things","volume":"8","author":"Zhao Yang","year":"2020","unstructured":"Yang Zhao , Jun Zhao , Mengmeng Yang , Teng Wang , Ning Wang , Lingjuan Lyu , Dusit Niyato , and Kwok-Yan Lam . 2020 . Local differential privacy-based federated learning for internet of things . IEEE Internet of Things Journal 8 , 11 (2020), 8836 -- 8853 . Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, and Kwok-Yan Lam. 2020. Local differential privacy-based federated learning for internet of things. IEEE Internet of Things Journal 8, 11 (2020), 8836--8853.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_2_1_87_1","volume-title":"Secure Shapley Value for Cross-Silo Federated Learning. arXiv preprint arXiv:2209.04856","author":"Zheng Shuyuan","year":"2022","unstructured":"Shuyuan Zheng , Yang Cao , and Masatoshi Yoshikawa . 2022. Secure Shapley Value for Cross-Silo Federated Learning. arXiv preprint arXiv:2209.04856 ( 2022 ). Shuyuan Zheng, Yang Cao, and Masatoshi Yoshikawa. 2022. Secure Shapley Value for Cross-Silo Federated Learning. arXiv preprint arXiv:2209.04856 (2022)."},{"key":"e_1_2_1_88_1","unstructured":"Ligeng Zhu Zhijian Liu and Song Han. 2019. Deep Leakage from Gradients. In Advances in Neural Information Processing Systems. 14747--14756.  Ligeng Zhu Zhijian Liu and Song Han. 2019. Deep Leakage from Gradients. In Advances in Neural Information Processing Systems. 14747--14756."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3587136.3587141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T23:12:32Z","timestamp":1683587552000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3587136.3587141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":86,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.14778\/3587136.3587141"],"URL":"https:\/\/doi.org\/10.14778\/3587136.3587141","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"2023-05-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}