Practical private-key fully homomorphic encryption in rings

Non-activation phase operations in ITER in hydrogen (H) and helium (He) will be important for commissioning of tokamak systems, such as diagnostics, heating and current drive (HCD) systems, coils and plasma control systems, and for validation of techniques necessary for establishing operations in DT. The assessment of feasible HCD schemes at various toroidal fields (2.65-5.3 T) has revealed that the previously applied assumptions need to be refined for the ITER non-activation phase H/He operations. A study of the ranges of plasma density and profile shape using the JINTRAC suite of codes has indicated that the hydrogen pellet fuelling into He plasmas should be utilized taking the optimization of IC power absorption, neutral beam shine-through density limit and H-mode access into account. The EPED1 estimation of the edge pedestal parameters has been extended to various H operation conditions, and the combined EPED1 and SOLPS estimation has provided guidance for modelling the edge pedestal in H/He operations. The availability of ITER HCD schemes, ranges of achievable plasma density and profile shape, and estimation of the edge pedestal parameters for H/He plasmas have been integrated into various time-dependent tokamak discharge simulations. In this work, various H/He scenarios at a wide range of plasma current (7.5-15 MA) and field (2.65-5.3 T) have been developed for the ITER non-activation phase operation, and the sensitivity of the developed scenarios to the used assumptions has been investigated to provide guidance for further development.

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“…Although the TF ripple will be slightly increased to 0.54% at 2.65 T [29], the TF ripple induced NB fast ion losses appear to stay at low level.…”

Section: Heating and Current Drivementioning

confidence: 92%

Development of ITER non-activation phase operation scenarios

et al. 2017

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“…The encrypted data and encrypted model (E2DM) framework performs classiication of encrypted data using an encrypted neural network model [115]. Implementation uses the HEAAN library and the square activation function [130].…”

Section: Namementioning

confidence: 99%

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

2020

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Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain. CCS Concepts: • Security and privacy → Cryptography; Usability in security and privacy; • Social and professional topics → Medical information policy.

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