This manuscript is the outcome of the work performed during the Homomorphic Encryption Standardization Strategic Planning Meeting 2020, held at Microsoft Research premises in Redmond, on February 5–6, 2020. This document describes how complex analysis tasks on sensitive data held by mutually untrusted data providers can be enabled by means of Multiparty Homomorphic Encryption (MHE), both in distributed and centralized environments. We showcase this approach in the medical sector, as it is a paradigmatic example where privacy is paramount and data sharing is needed. We show that MHE can be used to efficiently streamline and facilitate data discovery (Raisaro JL, Troncoso-Pastoriza JR, Misbach M, Sousa JS, Pradervand S, Missiaglia E, Michielin O, Ford B, Hubaux JP, IEEE/ACM Trans Comput Biol Bioinf 16(4):1328–1341, https://doi.org/10.1109/TCBB.2018.2854776, 2019; Froelicher D, Egger P, Sousa JS, Raisaro JL, Huang Z, Mouchet C, Ford B, Hubaux JP, UnLynx: a decentralized system for privacy-conscious data sharing. In: Proceedings on privacy enhancing technologies, vol 4, no. EPFL-CONF-229308, pp 152–170, 2017) and complex analysis (Froelicher D, Troncoso-Pastoriza JR, Sousa JS, Hubaux JP, IEEE Trans Inf Forensics Secur, 2020; Froelicher D, Troncoso-Pastoriza J, Pyrgelis A, Sav S, Sa Sousa J, Bossuat J-P, Hubaux J-P, Scalable privacy-preserving distributed learning. In: Proceedings on privacy enhancing technologies, vol 2, 2021; Aloufi A, Hu P, Wong HWH, Chow SSM, Blindfolded evaluation of random forests with multi-key homomorphic encryption. In: IEEE Transactions on Dependable and Secure Computing (TDSC), September 2019; Sinem S, Pyrgelis A, Troncoso-Pastoriza JR, Froelicher D, Bossuat J-P, Sa Sousa J, Hubaux JP, POSEIDON: privacy-preserving federated neural network learning. Accepted at NDSS 2021), e.g., training and evaluation of machine learning models, in environments in which the data are particularly sensitive, thus enabling secure collaborations in domains in which data sharing is usually difficult or even impossible with traditional technologies.
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Troncoso-Pastoriza, J., Froelicher, D., Hu, P., Aloufi, A., Hubaux, JP. (2021). Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption. In: Lauter, K., Dai, W., Laine, K. (eds) Protecting Privacy through Homomorphic Encryption. Springer, Cham. https://doi.org/10.1007/978-3-030-77287-1_3
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