Clinical Cancer Research; Federated Learning; General Data Protection Regulation; Patient Data Privacy; Privacy Enhancing Technologies; Privacy Metrics; Cancer research; Clinical cancer research; Cross-border; Federated learning; General data protection regulations; Multi-site; Patient data; Patient data privacy; Privacy enhancing technologies; Privacy metric; Artificial Intelligence; Computer Networks and Communications; Information Systems; Safety, Risk, Reliability and Quality
Abstract :
[en] This paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research has cast a shadow of doubt on its strength for privacy protection. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross-border European datasets with privacy guarantees that follow the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients' privacy.
Disciplines :
Computer science
Author, co-author :
Pedrouzo-Ulloa, Alberto; AtlanTTic Research Center, Universidade de Vigo, Vigo, Spain
Ramon, Jan; Institut National de Recherche en Informatique et Automatique (INRIA), Lille, France
Pérez-González, Fernando; AtlanTTic Research Center, Universidade de Vigo, Vigo, Spain
Lilova, Siyanna; Timelex, Brussels, Belgium
Duflot, Patrick ; Centre Hospitalier Universitaire de Liège - CHU > > Secteur Appui méthodologique aux Projets GSI et Planification (APP)
Chihani, Zakaria; CEA-List, Palaiseau, France
Gentili, Nicola; Istituto Scientifico Romagnolo per Lo Studio e la Cura Dei Tumori (IRST) IRCCS, Meldola, Italy
Ulivi, Paola; Istituto Scientifico Romagnolo per Lo Studio e la Cura Dei Tumori (IRST) IRCCS, Meldola, Italy
Hoque, Mohammad Ashadul; Technovative Solutions, Manchester, United Kingdom
Mukammel, Twaha; Technovative Solutions, Manchester, United Kingdom
Pritzker, Zeev; Arteevo Technologies, Tel Aviv, Israel
Lemesle, Augustin; Galician Research and Development Center in Advanced Telecommunications (GRADIANT), Vigo, Spain
Loureiro-Acuña, Jaime; Galician Research and Development Center in Advanced Telecommunications (GRADIANT), Vigo, Spain
Martínez, Xavier; Galician Research and Development Center in Advanced Telecommunications (GRADIANT), Vigo, Spain
Jiménez-Balsa, Gonzalo; Galician Research and Development Center in Advanced Telecommunications (GRADIANT), Vigo, Spain
Trumpet project was funded by the European Union with grant agreement Nr.101070038.; Binare; Digital Research Infrastructure for the Arts and Humanities (DARIAH-IT); IEEE Systems, Man, and Cybernetics Society (SMC); IEEE; LOGOS Research and Innovation (RI); Thales
M. Khan, F. G. Glavin, and M. Nickles, "Federated learning as a privacy solution-an overview, " Procedia Computer Science, vol. 217, pp. 316-325, 2023, 4th International Conference on Industry 4.0 and Smart Manufacturing.
T. Yang, G. Andrew, H. Eichner, H. Sun, W. Li, N. Kong, D. Ramage, and F. Beaufays, "Applied federated learning: Improving google keyboard query suggestions, " CoRR, vol. abs/1812.02903, 2018. [Online]. Available: Http://arxiv.org/abs/1812.02903
D. Leroy, A. Coucke, T. Lavril, T. Gisselbrecht, and J. Dureau, "Federated learning for keyword spotting, " in IEEE ICASSP. IEEE, 2019, pp. 6341-6345.
J. Ogier du Terrail, A. Leopold, C. Joly, C. Béguier, M. Andreux, C. Maussion, B. Schmauch, E. W. Tramel, E. Bendjebbar, M. Zaslavskiy, G. Wainrib, M. Milder, J. Gervasoni, J. Guerin, T. Durand, A. Livartowski, K. Moutet, C. Gautier, I. Djafar, A.-L. Moisson, C. Marini, M. Galtier, F. Balazard, R. Dubois, J. Moreira, A. Simon, D. Drubay, M. Lacroix-Triki, C. Franchet, G. Bataillon, and P.-E. Heudel, "Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer, " Nature Medicine, vol. 29, no. 3-4, pp. 135-146, 2023.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. A. Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D'Oliveira, H. Eichner, 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, H. Qi, D. Ramage, R. Raskar, M. Raykova, 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, " Found. Trends Mach. Learn., vol. 14, no. 1-2, pp. 1-210, 2021.
P. Vanhaesebrouck, A. Bellet, and M. Tommasi, "Decentralized collaborative learning of personalized models over networks, " in AISTATS, ser. Proceedings of Machine Learning Research, vol. 54. PMLR, 2017, pp. 509-517.
H. Tang, X. Lian, M. Yan, C. Zhang, and J. Liu, "D2: Decentralized training over decentralized data, " in ICML, ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 4855-4863.
B. E. Woodworth, J. Wang, A. D. Smith, B. McMahan, and N. Srebro, "Graph oracle models, lower bounds, and gaps for parallel stochastic optimization, " in NeurIPS 2018, 2018, pp. 8505-8515.
L. Melis, C. Song, E. D. Cristofaro, and V. Shmatikov, "Exploiting unintended feature leakage in collaborative learning, " in IEEE Symposium on Security and Privacy. IEEE, 2019, pp. 691-706.
M. Mansouri, M. Önen, W. B. Jaballah, and M. Conti, "Sok: Secure aggregation based on cryptographic schemes for federated learning, " Proc. Priv. Enhancing Technol., vol. 2023, no. 1, pp. 140-157, 2023.
P. Blanchard, E. M. E. Mhamdi, R. Guerraoui, and J. Stainer, "Machine learning with adversaries: Byzantine tolerant gradient descent, " in NIPS, 2017, pp. 119-129.
M. Nasr, R. Shokri, and A. Houmansadr, "Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning, " in IEEE Symposium on Security and Privacy. IEEE, 2019, pp. 739-753.
J. Girard-Satabin, M. Alberti, F. Bobot, Z. Chihani, and A. Lemesle, "Caisar: A platform for characterizing artificial intelligence safety and robustness." To appear in the proceedings of IJCAI's AISafety workshop, 2022. [Online]. Available: Https://arxiv.org/abs/2206.03044
J. Mattioli, S. Henri, D. Agnes, A.-F. Kahina, A. Afef, C. Zakaria, S. Khalfaoui, and G. Pedroza, "An overview of key trustworthiness attributes and kpis for trusted ml-based systems engineering, " AI Trustworthiness Assessment, 2023.
Big Data UN Global Working Group, "UN Handbook on Privacy-Preserving Computation Techniques, " https://unstats.un.org/bigdata/task-teams/privacy/index.cshtml, 2022, [Online; accessed 31-March-2023].
R. C. Geyer, T. Klein, and M. Nabi, "Differentially private federated learning: A client level perspective, " CoRR, vol. abs/1712.07557, 2017. [Online]. Available: Http://arxiv.org/abs/1712.07557
M. Chase, H. Chen, J. Ding, S. Goldwasser, S. Gorbunov, J. Hoffstein, K. Lauter, S. Lokam, D. Moody, T. Morrison, A. Sahai, and V. Vaikuntanathan, "Security of homomorphic encryption, " HomomorphicEncryption.org, Redmond, WA, Tech. Rep., July 2017.
J. Cabrero-Holgueras and S. Pastrana, "Sok: Privacy-preserving computation techniques for deep learning, " Proc. Priv. Enhancing Technol., vol. 2021, no. 4, pp. 139-162, 2021.
D. Evans, V. Kolesnikov, and M. Rosulek, "A pragmatic introduction to secure multi-party computation, " Foundations and TrendsR in Privacy and Security, vol. 2, pp. 70-246, 2018.
I. Damgård, M. Keller, E. Larraia, V. Pastro, P. Scholl, and N. P. Smart, "Practical covertly secure MPC for dishonest majority-or: Breaking the SPDZ limits, " in ESORICS, ser. Lecture Notes in Computer Science, vol. 8134. Springer, 2013, pp. 1-18.
C. Dwork, "Differential privacy, " in International Colloquium on Automata, Languages, and Programming. Berlin, Heidelberg: Springer, 2006, pp. 1-12.
J. S. Ng, W. Y. B. Lim, N. C. Luong, Z. Xiong, A. Asheralieva, D. Niyato, C. Leung, and C. Miao, "A survey of coded distributed computing, " vol. abs/2008.09048, 2020. [Online]. Available: Https://arxiv.org/abs/2008.09048
S. Ulukus, S. Avestimehr, M. Gastpar, S. Jafar, R. Tandon, and C. Tian, "Private retrieval, computing and learning: Recent progress and future challenges, " vol. abs/2108.00026, 2021. [Online]. Available: Https://arxiv.org/abs/2108.00026
C. Dwork and A. Roth, "The Algorithmic Foundations of Differential Privacy, " Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3-4, pp. 1-277, 2014.
D. Kifer and A. Machanavajjhala, "Pufferfish: A framework for mathematical privacy definitions, " ACM Transactions on Database Systems, vol. 39, no. 1, pp. 3:1-3:36, 2014.
A. Blum, K. Ligett, and A. Roth, "A learning theory approach to non-interactive database privacy, " in Proceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, British Columbia, Canada, May 17-20, 2008. ACM, 2008, pp. 609-618.
M. Nasr, R. Shokri, and A. Houmansadr, "Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning, " in 2019 IEEE Symposium on Security and Privacy, SP 2019, San Francisco, CA, USA, May 19-23, 2019. IEEE, 2019, pp. 739-753.
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramèr, "Membership inference attacks from first principles, " in 43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022. IEEE, 2022, pp. 1897-1914.
S. Rossello, R. D. Morales, and L. Muñoz-González, "Data Protection by design in AI?" Computerrecht: Tijdschrift voor informatica en recht, pp. 1-11, 2021.
Norwegian Data Protection Authority, "Report on Artificial intelligence and privacy, " https://www.datatilsynet.no/globalassets/global/english/ai-and-privacy.pdf, 2018, [Online; accessed 31-March-2023].
R. S. Vanguri, J. Luo, A. T. Aukerman, J. V. Egger, C. J. Fong, N. Horvat, A. Pagano, J. d. A. B. Araujo-Filho, L. Geneslaw, H. Rizvi, R. Sosa, K. M. Boehm, S.-R. Yang, F. M. Bodd, K. Ventura, T. J. Hollmann, M. S. Ginsberg, J. Gao, R. Vanguri, M. D. Hellmann, J. L. Sauter, S. P. Shah, and M. M. Consortium, "Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer, " Nature Cancer, vol. 3, no. 10, pp. 1151-1164, 2022.
S. Ramadan, K. Quan, K. Schnarr, R. A. Juergens, S. J. Hotte, S. D. Mukherjee, A. Kapoor, B. M. Meyers, and A. Swaminath, "Impact of stereotactic body radiotherapy (sbrt) in oligoprogressive metastatic disease, " Acta Oncologica, vol. 61, no. 6, pp. 705-713, 2022.
F. Siddiqui and B. Movsas, "Management of radiation toxicity in head and neck cancers, " Seminars in Radiation Oncology, vol. 27, no. 4, pp. 340-349, 2017.