A design space exploration of the countermeasures for hardware masking is proposed in this paper. The assumption of independence among shares used in hardware masking can be violated in practical designs. Recently, the security impact of noise coupling among multiple masking shares has been demonstrated both in practical FPGA implementations and with extensive transistor level simulations. Due to the highly sophisticated interactions in modern VLSI circuits, the interactions among multiple masking shares are quite challenging to model and thus information leakage from one share to another through noise coupling is difficult to mitigate. In this paper, the implications of utilizing on-chip voltage regulators to minimize the coupling among multiple masking shares through a shared power delivery network (PDN) are investigated. Specifically, different voltage regulator configurations where the power is delivered to different shares through various configurations are investigated. The placement of a voltage regulator relative to the masking shares is demonstrated to a have a significant impact on the coupling between masking shares. A PDN consisting of two shares is simulated with an ideal voltage regulator, strong DLDO, normal DLDO, weak DLDO, two DLDOs, and two DLDOs with 180∘ phase shift. An 18 × 18 grid PDN with a normal DLDO is simulated to demonstrate the effect of PDN impedance on security. The security analysis is performed using correlation and -test analyses where a low correlation between shares can be inferred as security improvement and a -test value below 4.5 means that the shares have negligible coupling, and thus the proposed method is secure. In certain cases, the proposed techniques achieve up to an 80% reduction in the correlation between masking shares. The PDN with two DLDOs and two-phase DLDO with 180∘ phase shift achieve satisfactory security levels since -test values remain under 4.5 with 100,000 traces of simulations. The security of the PDN improves if DLDO is placed closer to any one of the masking shares.
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http://dx.doi.org/10.3390/s22187028 | DOI Listing |
Radiother Oncol
December 2024
Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing 400038, China. Electronic address:
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View Article and Find Full Text PDFNurs Health Sci
December 2024
School of Nursing and Midwifery, Deakin University, Geelong, Australia.
Experiencing side effects when wearing N95/P2 masks has negative impacts on health workers and increases exposure to pathogens. While side effects of wearing P2/N95 masks have been reported previously, these masks have never been used as widely as during the COVID-19 pandemic. This study examines Australian hospital nurses' experiences and perceptions of P2/N95 mask usage and its impacts on patient care at a time when P2/N95 masks were widely used.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
Uppsala Monitoring Centre, Uppsala, Sweden.
Background: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning models have not sufficiently focused on the importance of active atoms and adsorbates, instead placing more emphasis on the overall structure of the catalytic materials.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Biological Sciences, University of Denver, Denver, CO 80208.
The discovery that sponges (Porifera) can fully regenerate from aggregates of dissociated cells launched them as one of the earliest experimental models to study the evolution of cell adhesion and allorecognition in animals. This process depends on an extracellular glycoprotein complex called the Aggregation Factor (AF), which is composed of proteins thought to be unique to sponges. We used quantitative proteomics to identify additional AF components and interacting proteins in the classical model, , and compared them to proteins involved in cell interactions in Bilateria.
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