Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

Reihaneh Torkzadehmahani; Reza Nasirigerdeh; David B. Blumenthal; Tim Kacprowski; Markus List; Julian Matschinske; Julian Spaeth; Nina Kerstin Wenke; Jan Baumbach

doi:10.1055/s-0041-1740630

Methods of Information in Medicine, Table of Contents

CC BY-NC-ND 4.0 · Methods Inf Med 2022; 61(S 01): e12-e27
DOI: 10.1055/s-0041-1740630

Original Article

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

Reihaneh Torkzadehmahani

¹Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany

,

Reza Nasirigerdeh

¹Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany

²Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany

,

David B. Blumenthal

³Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany

,

Tim Kacprowski

⁴Division of Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Medical School Hannover, Braunschweig, Germany

⁵Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany

,

Markus List

⁶Chair of Experimental Bioinformatics, Technical University of Munich, Munich, Germany

,

Julian Matschinske

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany

,

Julian Spaeth

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany

,

Nina Kerstin Wenke

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany

,

Jan Baumbach

⁷E.U. Horizon2020 FeatureCloud Project Consortium

⁸Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany

⁹Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

› Author Affiliations

Abstract

Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems.

Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy.

Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.

Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.

Keywords

privacy-preserving AI techniques - federated learning - biomedicine

Full Text

References

References
1 Schwarting W, Alonso-Mora J, Rus D. Planning and decision-making for autonomous vehicles. Annu Rev Control Robot Auton Syst 2018; 1 (01) 187-210
2 Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN. Convolutional sequence to sequence learning. Paper presented at: Proceedings of the 34th International Conference on Machine Learning. JMLR. Org. Volume 70: 1243-1252 2017
3 Xiong W, Wu L, Alleva F, Droppo J, Huang X, Stolcke A. The Microsoft 2017 conversational speech recognition system. Paper presented at: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2018: 5934-5938
4 Holzinger A, Kieseberg P, Weippl E, Tjoa AM. Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI in Springer Lecture Notes in Computer Science LNCS 11015. Cham: Springer; 2018: 1-8
5 Gómez-Bombarelli R, Wei JN, Duvenaud D. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 2018; 4 (02) 268-276
6 Ma J, Yu MK, Fong S. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 2018; 15 (04) 290-298
7 Nie D, Trullo R, Lian J. et al. Medical image synthesis with deep convolutional adversarial networks. IEEE Trans Biomed Eng 2018; 65 (12) 2720-2730
8 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
9 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018; 319 (13) 1317-1318
10 Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2 (10) 719-731
11 Yu MK, Ma J, Fisher J, Kreisberg JF, Raphael BJ, Ideker T. Visible machine learning for biomedicine. Cell 2018; 173 (07) 1562-1565
12 Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23 (06) 1241-1250
13 Wainberg M, Merico D, Delong A, Frey BJ. Deep learning in biomedicine. Nat Biotechnol 2018; 36 (09) 829-838
14 Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform 2017; 18 (05) 851-869
15 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
16 Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19: 221-248
17 Jiang F, Jiang Y, Zhi H. et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2 (04) 230-243
18 Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet 2015; 16 (06) 321-332
19 Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 2018; 46 (04) 547-553
20 Teare P, Fishman M, Benzaquen O, Toledano E, Elnekave E. Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J Digit Imaging 2017; 30 (04) 499-505
21 Veta M, van Diest PJ, Willems SM. et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal 2015; 20 (01) 237-248
22 Naveed M, Ayday E, Clayton EW. et al. Privacy in the genomic era. ACM Comput Surv 2015; 48 (01) 1-44
23 Shokri R, Stronati M, Song C, Shmatikov V. Membership inference attacks against machine learning models. Paper presented at: 2017 IEEE Symposium on Security and Privacy (SP). IEEE; 2017: 3-18
24 Papernot N, McDaniel P, Sinha A, Wellman MP. SoK: Security and privacy in machine learning. Paper presented at: 2018 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE; 2018: 399-414
25 Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning requires rethinking generalization. Paper presented at: Proceedings of the International Conference on Learning Representations (ICLR); 2017
26 Zhang Y, Jia R, Pei H, Wang W, Li B, Song D. The secret revealer: generative model-inversion attacks against deep neural networks. Paper presented at: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020: 253-261
27 Shringarpure SS, Bustamante CD. Privacy risks from genomic data-sharing beacons. Am J Hum Genet 2015; 97 (05) 631-646
28 Homer N, Szelinger S, Redman M. et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet 2008; 4 (08) e1000167
29 Harmanci A, Gerstein M. Analysis of sensitive information leakage in functional genomics signal profiles through genomic deletions. Nat Commun 2018; 9 (01) 2453
30 Wang R, Li YF, Wang X, Tang H, Zhou X. Learning your identity and disease from research papers: information leaks in genome wide association study. Paper presented at: Proceedings of the 16th ACM Conference on Computer and Communications Security; 2009: 534-544
31 Genomics GA. Global Alliance for Genomics and Health. GENOMICS. A federated ecosystem for sharing genomic, clinical data. Science 2016; 352 (6291): 1278-1280
32 Humbert M, Ayday E, Hubaux J-P, Telenti A. Addressing the concerns of the lacks family: quantification of kin genomic privacy. Paper presented at: Proceedings of the 2013 ACM SIGSAC Conference on Computer& Communications Security; 2013: 1141-1152
33 Zerhouni EA, Nabel EG. Protecting aggregate genomic data. Science 2008; 322 (5898): 44-44
34 Erlich Y, Narayanan A. Routes for breaching and protecting genetic privacy. Nat Rev Genet 2014; 15 (06) 409-421
35 General Data Protection Regulation(GDPR). . Accessed May 15, 2020 at: https://gdpr-info.eu/ 2020
36 Cohen A, Nissim K. Towards formalizing the GDPR's notion of singling out. Paper presented at: Proceedings of the National Academy of Sciences; 2020
37 Aziz MMA, Sadat MN, Alhadidi D. et al. Privacy-preserving techniques of genomic data—a survey. Brief Bioinform 2019; 20 (03) 887-895
38 Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F. Federated learning for healthcare informatics. J Healthc Inform Res 2020; 5 (01) 1-19
39 Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell 2021; 3: 474-484
40 Chen F, Wang S, Jiang X. et al. PRINCESS: privacy-protecting rare disease international network collaboration via encryption through software guard extensionS. Bioinformatics 2017; 33 (06) 871-878
41 Chen F. et al. Premix: privacy-preserving estimation of individual admixture. Paper presented at: AMIA Annual Symposium Proceedings, vol. 2016. American Medical Informatics Association; 2016: 1747
42 Fuhry B. et al. Hardidx: Practical and Secure Index with sgx. Paper presented at: IFIP Annual Conference on Data and Applications Security and Privacy. Springer; 2017: 386-408
43 Kaplan D, Powell J, Woller T. AMD memory encryption. White paper; Accessed April, 2016 at: http://developer.amd.com/wordpress/media/2013/12/AMD_Memory_Encryption_Whitepaper_v7-Public.pdf
44 Cho H, Wu DJ, Berger B. Secure genome-wide association analysis using multiparty computation. Nat Biotechnol 2018; 36 (06) 547-551
45 Bonte C, Makri E, Ardeshirdavani A, Simm J, Moreau Y, Vercauteren F. Towards practical privacy-preserving genome-wide association study. BMC Bioinformatics 2018; 19 (01) 537
46 Jagadeesh KA, Wu DJ, Birgmeier JA, Boneh D, Bejerano G. Keeping patient phenotypes and genotypes private while seeking disease diagnoses. bioRxiv 2019; 746230
47 Kim M, Lauter K. Private Genome Analysis through Homomorphic Encryption. In: BMC Medical Informatics and Decision Making; 15:S3 Springer; 2015
48 Lauter K, López-Alt A, Naehrig M. Private Computation on Encrypted Genomic Data. Paper presented at: International Conference on Cryptology and Information Security in Latin America. Springer; 2014: 3-27
49 Lu WJ, Yamada Y, Sakuma J. Privacy-Preserving Genome-Wide Association Studies on Cloud Environment Using Fully Homomorphic Encryption. In: BMC Medical Informatics and Decision Making;15:S1 Springer; 2015
50 Zhang Y, Dai W, Jiang X, Xiong H, Wang S. Foresee: Fully Outsourced Secure Genome Study Based on Homomorphic Encryption. In: BMC Medical Informatics and Decision Making;15:S5 Springer; 2015
51 Kamm L, Bogdanov D, Laur S, Vilo J. A new way to protect privacy in large-scale genome-wide association studies. Bioinformatics 2013; 29 (07) 886-893
52 Constable SD, Tang Y, Wang S, Jiang X, Chapin S. Privacy-preserving GWAS analysis on federated genomic datasets. In: BMC Medical Informatics and Decision Making; 15:S2 Springer; 2015
53 Zhang Y, Blanton M, Almashaqbeh G. Secure distributed genome analysis for GWAS and sequence comparison computation. In: BMC Medical Informatics and Decision Making; 15:S4 BioMed Central; 2015
54 Mohassel P, Zhang Y. Secureml: a system for scalable privacy-preserving machine learning. Paper presented at: 2017 IEEE Symposium on Security and Privacy (SP). IEEE; 2017: 19-38
55 Hasan Z, Mahdi MSR, Mohammed N. Secure count query on encrypted genomic data: a survey. IEEE Internet Comput 2018; 22 (02) 71-82
56 Sadat MN, Al Aziz MM, Mohammed N, Chen F, Jiang X, Wang S. SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16 (01) 93-102
57 Gentry C. Fully homomorphic encryption using ideal lattices Paper presented at: Proceedings of the 41st Annual ACM Symposium on Theory of Computing. 2009: 169-178
58 Paillier P. Public-key cryptosystems based on composite degree residuosity classes. Paper presented at: International Conference on the Theory and Applications of Cryptographic Techniques; 1999: 223-238
59 Gennaro R, Rabin MO, Rabin T. Simplified vss and Fast-Track Multiparty Computations with Applications to Threshold Cryptography. Paper presented at: Proceedings of the 17th annual ACM Symposium on Principles of Distributed Computing; 1998: 101-111
60 Cramer R, Damgård IB, Nielsen JB. Secure Multiparty Computation. Cambridge University Press; 2015
61 Shamir A. How to share a secret. Commun ACM 1979; 22 (11) 612-613
62 Rabin MO. How to exchange secrets with oblivious transfer. IACR Cryptol. ePrint Arch 2005 2005(187)
63 Yao AC-C. How to generate and exchange secrets. Paper presented at: 27th Annual Symposium on Foundations of Computer Science (sfcs 1986). IEEE; 1986: 162-167
64 Boneh D, Boyen X, Halevi S. Chosen ciphertext secure public key threshold encryption without random oracles. Paper presented at: Cryptographers' Track at the RSA Conference. Springer; 2006: 226-243
65 Kim M, Song Y, Wang S, Xia Y, Jiang X. Secure logistic regression based on homomorphic encryption: design and evaluation. JMIR Med Inform 2018; 6 (02) e19
66 Morshed T, Alhadidi D, Mohammed N. Parallel linear regression on encrypted data. Paper presented at: 2018 16th Annual Conference on Privacy, Security and Trust (PST). IEEE; 2018: 1-5
67 Shi H, Jiang C, Dai W. et al. Secure multi-pArty computation grid LOgistic REgression (SMAC-GLORE). BMC Med Inform Decis Mak 2016; 16 (03, Suppl 3): 89
68 Bloom JM. Secure multi-party linear regression at plaintext speed. arXiv preprint arXiv:1901.09531; 2019
69 Mittos A, Malin B, De Cristofaro E. Systematizing genome privacy research: a privacy-enhancing technologies perspective. Proc Privacy Enhancing Technol 2019; 2019 (01) 87-107
70 Berger B, Cho H. Emerging technologies towards enhancing privacy in genomic data sharing. Genome Biol 2019; 20 (01) 128
71 Chialva D, Dooms A. Conditionals in homomorphic encryption and machine learning applications. arXiv preprint arXiv:1810.12380. 2018
72 Alexandru AB, Pappas GJ. Secure Multi-party Computation for Cloud-Based Control. In: Privacy in Dynamical Systems 2020 (pp. 179-207). Singapore: Springer
73 Dwork C, McSherry F, Nissim K, Smith A. Calibrating noise to sensitivity in private data analysis. J Privacy Confidentiality 2016; 7 (03) 17-51
74 Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M. Our data, ourselves: privacy via distributed noise generation. Paper presented at: Annual International Conference on the Theory and Applications of Cryptographic Techniques. Springer; 2006: 486-503
75 Nissim K, Steinke T, Wood A. et al. Differential privacy: a primer for a non-technical audience. Paper presented at: Privacy Law Scholars Conf; 2017
76 Erlingsson Ú, Pihur V, Korolova A. Rappor: Randomized aggregatable privacy-preserving ordinal response. Paper presented at: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security; 2014: 1054-1067
77 Thakurta AG, Vyrros AH, Vaishampayan US. et al. Learning new words. 2017 . US Patent 9,594,741
78 Johnson A, Shmatikov V. Privacy-preserving data exploration in genome-wide association studies. Paper presented at: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining; 2013: 1079-1087
79 Aziz MMA, Ghasemi R, Waliullah M, Mohammed N. Aftermath of bustamante attack on genomic beacon service. BMC Med Genomics 2017; 10 (02, Suppl 2): 43
80 Han Z, Lu L, Liu H. A differential privacy preserving approach for logistic regression in genome-wide association studies. Paper presented at: 2019 International Conference on Networking and Network Applications (NaNA). IEEE; 2019: 181-185
81 Yu F, Rybar M, Uhler C, Fienberg SE. Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. Paper presented at: International Conference on Privacy in Statistical Databases. Springer; 2014: 170-184
82 Honkela A, Das M, Nieminen A, Dikmen O, Kaski S. Efficient differentially private learning improves drug sensitivity prediction. Biol Direct 2018; 13 (01) 1
83 Simmons S, Sahinalp C, Berger B. Enabling privacy-preserving GWASs in heterogeneous human populations. Cell Syst 2016; 3 (01) 54-61
84 Simmons S, Berger B. Realizing privacy preserving genome-wide association studies. Bioinformatics 2016; 32 (09) 1293-1300
85 Fienberg SE, Slavkovic A, Uhler C. Privacy preserving GWAS data sharing. Paper presented at: 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE; 2011: 628-635
86 Uhlerop C, Slavković A, Fienberg SE. Privacy-preserving data sharing for genome-wide association studies. J Priv Confid 2013; 5 (01) 137-166
87 Yu F, Ji Z. Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge. BMC Med Inform Decis Mak 2014; 14 (01, Suppl 1): S3
88 Wang S, Mohammed N, Chen R. Differentially private genome data dissemination through top-down specialization. BMC Med Inform Decis Mak 2014; 14 (01, Suppl 1): S2
89 Beaulieu-Jones BK, Yuan W, Finlayson SG, Wu ZS. Privacy-preserving distributed deep learning for clinical data. arXiv preprint arXiv: 1812.01484. 2018
90 Han Z, Liu H, Wu Z. A differential privacy preserving framework with nash equilibrium in genome-wide association studies. Paper presented at: 2018 International Conference on Networking and Network Applications (NaNA). IEEE; 2018: 91-96
91 Tramèr F, Huang Z, Hubaux JP, Ayday E. Differential privacy with bounded priors: reconciling utility and privacy in genome-wide association studies. Paper presented at: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security; 2015: 1286-1297
92 Vu D, Slavkovic A. Differential privacy for clinical trial data: preliminary evaluations. Paper presented at: 2009 IEEE International Conference on Data Mining Workshops. IEEE; 2009: 138-143
93 Wan Z, Vorobeychik Y, Kantarcioglu M, Malin B. Controlling the signal: practical privacy protection of genomic data sharing through Beacon services. BMC Med Genomics 2017; 10 (02, Suppl 2): 39
94 Kairouz P, Oh S, Viswanath P. The composition theorem for differential privacy. IEEE Trans Inf Theory 2017; 63 (06) 4037-4049
95 Cho H, Simmons S, Kim R, Berger B. Privacy-preserving biomedical database queries with optimal privacy-utility trade-offs. Cell Syst 2020; 10 (05) 408-416.e9
96 Ji Z, Jiang X, Wang S, Xiong L, Ohno-Machado L. Differentially private distributed logistic regression using private and public data. BMC Med Genomics 2014; 7 (Suppl. 01) S14
97 Abay NC, Zhou Y, Kantarcioglu M, Thuraisingham B, Sweeney L. Privacy preserving synthetic data release using deep learning. Paper presented at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer; 2018: 510-526
98 Beaulieu-Jones BK, Wu ZS, Williams C. et al. Privacy-preserving generative deep neural networks support clinical data sharing. Circ Cardiovasc Qual Outcomes 2019; 12 (07) e005122
99 Jordon J, Yoon J, Van Der Schaar M. PATE-GAN: Generating synthetic data with differential privacy guarantees. In International conference on learning representations 2018 Sep 27
100 Fiume M, Cupak M, Keenan S. et al. Federated discovery and sharing of genomic data using Beacons. Nat Biotechnol 2019; 37 (03) 220-224
101 Raisaro JL, Gwangbae Choi, Pradervand S. et al. Protecting privacy and security of genomic data in I2B2 with homomorphic encryption and differential privacy. IEEE/ACM Trans Comput Biol Bioinformatics 2018; 15 (05) 1413-1426
102 Hardt M, Ligett K, McSherry F. A simple and practical algorithm for differentially private data release. In Advances in Neural Information Processing Systems, pp. 2339–2347, 2012
103 Raisaro JL, Troncoso-Pastoriza JR, Misbach M. et al. MedCo: enabling secure and privacy-preserving exploration of distributed clinical and genomic data. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16 (04) 1328-1341
104 Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38 (08) 904-909
105 Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet 2014; 46 (02) 100-106
106 Goodfellow I, Pouget-Abadie J, Mirza M. et al. Generative adversarial networks. Communications of the ACM 2020; 22; 63 (11) 139-144
107 Wang S, Jiang X, Singh S. et al. Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Ann N Y Acad Sci 2017; 1387 (01) 73-83
108 Kieseberg P, Hobel H, Schrittwieser S, Weippl E, Holzinger A. Protecting anonymity in data-driven biomedical science. In: In: Holzinger A, Jurisica I. eds. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Lecture Notes in Computer Science, LNCS 8401. Berlin Heidelberg: Springer; 2014: 301-316
109 McMahan B, Moore E, Ramage D, Hampson S, Arcas BAy. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. PMLR 2017; 54: 1273-1282
110 Kairouz P, McMahan HB, Avent B. et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977. 2019
111 Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 2019; 10 (02) 1-19
112 The TFF Authors. Tensor flow federated. 2019 . Accessed June 18 2020 at: https://www.tensorflow.org/federated
113 Ryffel T, Trask A, Dahl M. et al. A generic framework for privacy preserving deep learning. arXiv preprint arXiv:1811.04017. 2018
114 The FATE Authors. Federated AI technology enabler. 2019 . Accessed June 18 2020 at: https://www.fedai.org/
115 The PaddleFL Authors. PaddleFL, 2019. Accessed June 18 2020 at: https://github.com/PaddlePaddle/PaddleFL
116 The FeatureCloud Authors. FeatureCloud, 2019. Accessed June 18 2020 at: https://featurecloud.eu/
117 Clara NVIDIA. The Clara training framework authors. 2019 . Accessed June 18 2020 at: https://developer.nvidia.com/clara-medical-imaging
118 Sheller MJ, Reina GA, Edwards B, Martin J, Bakas S. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. Paper presented at: International MICCAI Brainlesion Workshop. Springer; 2018: 92-104
119 Vepakomma P, Gupta O, Swedish T, Raskar R. Split learning for health: distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564. 2018
120 Vepakomma P, Gupta O, Dubey A, Raskar R. Reducing leakage in distributed deep learning for sensitive health data. arXiv preprint arXiv:1812.00564. 2019
121 Poirot MG, Vepakomma P, Chang K, Kalpathy-Cramer J, Gupta R, Raskar R. Split Learning for collaborative deep learning in healthcare. arXiv preprint arXiv:1912.12115. 2019
122 Balachandar N, Chang K, Kalpathy-Cramer J, Rubin DL. Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J Am Med Inform Assoc 2020; 27 (05) 700-708
123 Chang K, Balachandar N, Lam C. et al. Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc 2018; 25 (08) 945-954
124 Dai W, Jiang X, Bonomi L, Li Y, Xiong H, Ohno-Machado L. VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers. IEEE Transactions on Knowledge and Data Engineering 2020 Apr 22
125 Lu CL, Wang S, Ji Z. et al. WebDISCO: a web service for distributed cox model learning without patient-level data sharing. J Am Med Inform Assoc 2015; 22 (06) 1212-1219
126 Wu Y, Jiang X, Kim J, Ohno-Machado L. Grid Binary LOgistic REgression (GLORE): building shared models without sharing data. J Am Med Inform Assoc 2012; 19 (05) 758-764
127 Wang S, Jiang X, Wu Y, Cui L, Cheng S, Ohno-Machado L. EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning. J Biomed Inform 2013; 46 (03) 480-496
128 Li Y, Jiang X, Wang S, Xiong H, Ohno-Machado L. VERTIcal Grid lOgistic regression (VERTIGO). J Am Med Inform Assoc 2016; 23 (03) 570-579
129 Nasirigerdeh R, Torkzadehmahani R, Matschinske J, Frisch T, List M, Späth J, Wei ß S, Völker U, Heider D, Wenke NK, Kacprowski T. sPLINK: a federated, privacy-preserving tool as a robust alternative to meta-analysis in genome-wide association studies. BioRxiv. 2020 Jan 1
130 Gabay D. Applications of the method of multipliers to variational inequalities. In: Studies in Mathematics and Its Applications. Elsevier 1983; 15: 299-331
131 Purcell S, Neale B, Todd-Brown K. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81 (03) 559-575
132 Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated electronic health records. Int J Med Inform 2018; 112: 59-67
133 Huang L, Yin Y, Fu Z, Zhang S, Deng H, Liu D. Loadaboost: loss-based adaboost federated machine learning on medical data. arXiv preprint arXiv: 1811.12629. 2018
134 Liu D, Miller T, Sayeed R, Mandl KD. Fadl: federated-autonomous deep learning for distributed electronic health record. arXiv preprint arXiv:1811.11400. 2018
135 Chen Y, Wang J, Yu C, Gao W, Qin X. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. arXiv preprint arXiv:1907.09173. 2019
136 Silva S, Gutman BA, Romero E, Thompson PM, Altmann A, Lorenzi M. Federated learning in distributed medical databases: meta-analysis of large-scale subcortical brain data. Paper presented at: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE; 2019
137 Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 2018; 5: 180178
138 Smith V, Chiang CK, Sanjabi M, Talwalkar AS. Federated multi-task learning. In: Advances in Neural Information Processing Systems 2017: 4424-4434
139 Corinzia L, Buhmann JM. Variational federated multi-task learning. arXiv preprint arXiv:1906.06268; 2019
140 Liu Y, Kang Y, Xing C, Chen T, Yang Q. A secure federated transfer learning framework. IEEE Intell Syst 2020; 35 (04) 70-82
141 Gupta S, Agrawal A, Gopalakrishnan K, Narayanan P. Deep learning with limited numerical precision. Paper presented at: International Conference on Machine Learning; 2015: 1737-1746
142 Aji AF, Heafield K. Sparse Communication for Distributed Gradient Descent. Paper presented at: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing; 2017: 440-445
143 McMahan HB, Moore E, Ramage D, Hampson S. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv: 1602.05629. 2016
144 Tang Z, Shi S, Chu X, Wang W, Li B. Communication-efficient distributed deep learning: a comprehensive survey. arXiv preprint arXiv:2003.06307. 2020
145 Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582. 2018
146 Li Q, Wen Z, He B. Federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693. 2019
147 Rieke N, Hancox J, Li W. et al. The future of digital health with federated learning. NPJ Digit Med 2020; 3 (01) 119
148 Melis L, Song C, De Cristofaro E, Shmatikov V. Exploiting unintended feature leakage in collaborative learning. Paper presented at: 2019 IEEE Symposium on Security and Privacy (SP). IEEE; 2019: 691-706
149 Li W, Milletarì F, Xu D. et al. Privacy-preserving federated brain tumour segmentation. Paper presented at: International Workshop on Machine Learning in Medical Imaging. Springer; 2019: 133-141
150 Li X, Gu Y, Dvornek N, Staib L, Ventola P, Duncan JS. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. arXiv preprint arXiv:2001.05647. 2020
151 Geyer RC, Klein T, Nabi M. Differentially private federated learning: a client level perspective. arXiv preprint arXiv:1712.07557. 2017
152 Truex S, Baracaldo N, Anwar A. et al. A hybrid approach to privacy-preserving federated learning. Paper presented at: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security; 2019: 1-11
153 Wei K, Li J, Ding M. et al. Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans Inf Forensics Security 2020; 15: 3454-3469
154 Hardy S, Henecka W, Ivey-Law H. et al. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677. 2017
155 Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y. BatchCrypt: efficient homomorphic encryption for cross-silo federated learning. Paper presented at: 2020 {USENIX} Annual Technical Conference ({USENIX}{ATC} 20) 493-506
156 Kim M, Lee J, Ohno-Machado L, Jiang X. Secure and differentially private logistic regression for horizontally distributed data. IEEE Trans Inf Forensics Security 2019; 15: 695-710
157 Froelicher D, Egger P, Sousa JS. et al. UnLynx: a decentralized system for privacy-conscious data sharing. Proceedings on Privacy Enhancing Technologies. 2017; 2017 (04) 232-250
158 Lee J, Sun J, Wang F, Wang S, Jun CH, Jiang X. Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR Med Inform 2018; 6 (02) e20
159 Choudhury O, Gkoulalas-Divanis A, Salonidis T. et al. Differential privacy-enabled federated learning for sensitive health data. arXiv preprint arXiv:1910.02578. 2019
160 Dankar FK, Madathil N, Dankar SK, Boughorbel S. Privacy-preserving analysis of distributed biomedical data: designing efficient and secure multiparty computations using distributed statistical learning theory. JMIR Med Inform 2019; 7 (02) e12702