HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE.Comment: 17 pages, 7 figure

ELECTRON: An Architectural Framework for Securing the Smart Electrical Grid with Federated Detection, Dynamic Risk Assessment and Self-Healing

The electrical grid has significantly evolved over the years, thus creating a smart paradigm, which is well known as the smart electrical grid. However, this evolution creates critical cybersecurity risks due to the vulnerable nature of the industrial systems and the involvement of new technologies. Therefore, in this paper, the ELECTRON architecture is presented as an integrated platform to detect, mitigate and prevent potential cyberthreats timely. ELECTRON combines both cybersecurity and energy defence mechanisms in a collaborative way. The key aspects of ELECTRON are (a) dynamic risk assessment, (b) asset certification, (c) federated intrusion detection and correlation, (d) Software Defined Networking (SDN) mitigation, (e) proactive islanding and (f) cybersecurity training and certification

Heterogeneous parametric trivariate fillets

Blending and filleting are well established operations in solid modeling and computer-aided geometric design. The creation of a transition surface which smoothly connects the boundary surfaces of two (or more) objects has been extensively investigated. In this work, we introduce several algorithms for the construction of, possibly heterogeneous, trivariate fillets, that support smooth filleting operations between pairs of, possibly heterogeneous, input trivariates. Several construction methods are introduced that employ functional composition algorithms as well as introduce a half Volumetric Boolean sum operation. A volumetric fillet, consisting of one or more tensor product trivariate(s), is fitted to the boundary surfaces of the input. The result smoothly blends between the two inputs, both geometrically and material-wise (properties of arbitrary dimension). The application of encoding heterogeneous material information into the constructed fillet is discussed and examples of all proposed algorithms are presented. (C) 2021 Elsevier B.V. All rights reserved