arXiv subjects

Vikas Srivastava

Publications and source records attributed to Vikas Srivastava.

25 records · Page 2Linked to original sources

arXiv2024

Quantum Secure Protocols for Multiparty Computations

Secure multiparty computation (MPC) schemes allow two or more parties to conjointly compute a function on their private input sets while revealing nothing but the output. Existing state-of-the-art number-theoretic-based designs face the threat of attacks through quantum algorithms. In this context, we present secure MPC protocols that can withstand quantum attacks. We first present the design and analysis of an information-theoretic secure oblivious linear evaluation (OLE), namely ${\sf qOLE}$ in the quantum domain, and show that our ${\sf qOLE}$ is safe from external attacks. In addition, our scheme satisfies all the security requirements of a secure OLE. We further utilize ${\sf qOLE}$ as a building block to construct a quantum-safe multiparty private set intersection (MPSI) protocol.

quant-ph↗

arXiv2024

A method to measure the embedded crack length and position in high-density polyethylene using microseconds ultrasound time signal

High-density polyethylene (HDPE) is used in applications ranging from cooling water pipelines in nuclear power plants and distribution pipelines for natural gas and hydrogen to biomedical implants. Embedded crack-like flaws form within HDPE during fabrication or operations. Non-visible flaws can cause catastrophic failure if undetected. Large structures such as HDPE pipelines require a fast, non-destructive evaluation (NDE) method where the sensor can move rapidly across the structure. This is only possible if the flaw is evaluated using microseconds of time signal. We propose and show the accuracy of a machine learning-based Ultrasound NDE method that can rapidly and accurately predict embedded crack length and position simultaneously in HDPE with only tens of microseconds of time signal sensing. A method to quantify crack size in HDPE and other polymers using a very short Ultrasound time signal is lacking. We propose that an optimally trained machine learning model can decipher the crack characteristics using short measures of time signal, but a lack of large, well-distributed, and labeled datasets to train machine learning models continues to be a major limitation. To overcome this limitation, we have conducted computer simulations of ultrasound on HDPE to develop training data. We show that fully simulations trained convolutional neural network (CNN) can accurately predict crack lengths and positions in HDPE from experimentally measured ultrasound A-scan microsecond signals. Our method is based on the 1D time amplitude signal acquired over a very short time period and not based on 2D image analysis. The proposed methodology presents a pathway for training CNN using computationally generated data and applying the trained CNN in the field to quantify hidden cracks in large HDPE or other polymer structures.

cond-mat.mtrl-sci↗

arXiv2024

Role of coupled electrochemistry and stress on the Li-anode instability: A continuum approach

We present a coupled mechanistic approach that elucidates the intricate interplay between stress and electrochemistry, enabling the prediction of the onset of instabilities in Li-metal anodes and the solid electrolyte interphase (SEI) in liquid-electrolyte Li-metal batteries. Our continuum theory considers a two-way coupling between stress and electrochemistry, includes Li and electron transport through SEI, incorporates effects of Li viscoplasticity, includes SEI and electrolyte interface surface energy and evaluates crucial roles of these mechanistic effects on the continuously evolving anode surface due to the viscoplastic deformation of lithium. In the model, spatial current density evolves with the stress-induced potential across the deformed anode/SEI interface. We assume SEI as a homogeneous, artificial layer on the Li-anode, which allows the investigation of the mechanical and electrochemical properties of the SEI systematically. Subsequently, we solve a set of coupled electrochemistry and displacement equations within the SEI and anode domains. The model is implemented numerically by writing a user element subroutine in Abaqus/Standard. We conduct numerical simulations under various galvanostatic conditions and SEI properties and predict conditions for anode instability. We find that Li viscoplasticity is one of the key attributes that drives instability in the Li-anode and show that applying a soft artificial SEI layer on the Li-anode to minimize viscoplastic deformation can be an effective method. We also report the role of artificial SEI elasticity and thickness on anode stability. Selected stability maps are provided as a design aid for artificial SEI.

math.NA↗

arXiv2024

An Experimentally Validated Feasible Quantum Protocol for Identity-Based Signature with Application to Secure Email Communication

Digital signatures are one of the simplest cryptographic building blocks that provide appealing security characteristics such as authenticity, unforgeability, and undeniability. In 1984, Shamir developed the first Identity-based signature (IBS) to simplify public key infrastructure and circumvent the need for certificates. It makes the process uncomplicated by enabling users to verify digital signatures using only the identifiers of signers, such as email, phone number, etc. Nearly all existing IBS protocols rely on several theoretical assumption-based hard problems. Unfortunately, these hard problems are unsafe and pose a hazard in the quantum realm. Thus, designing IBS algorithms that can withstand quantum attacks and ensure long-term security is an important direction for future research. Quantum cryptography (QC) is one such approach. In this paper, we propose an IBS based on QC. Our scheme's security is based on the laws of quantum mechanics. It thereby achieves long-term security and provides resistance against quantum attacks. We verify the proposed design's correctness and feasibility by simulating it in a prototype quantum device and the IBM Qiskit quantum simulator. The implementation code in qiskit with Jupyternotebook is provided in the Annexure. Moreover, we discuss the application of our design in secure email communication.

cs.CR↗

arXiv2024

Blockchain-Envisioned Post-Quantum Secure Sanitizable Signature for Audit Logs Management

Audit logs are one of the most important tools for transparently tracking system events and maintaining continuous oversight in corporate organizations and enterprise business systems. There are many cases where the audit logs contain sensitive data, or the audit logs are enormous. In these situations, dealing with a subset of the data is more practical than the entire data set. To provide a secure solution to handle these issues, a sanitizable signature scheme (SSS) is a viable cryptographic primitive. Herein, we first present the first post-quantum secure multivariate-based SSS, namely Mul-SAN. Our proposed design provides unforgeability, privacy, immutability, signer accountability, and sanitizer accountability under the assumption that the MQ problem is NP-hard. Mul-SAN is very efficient and only requires computing field multiplications and additions over a finite field for its implementation. Mul-SAN presents itself as a practical method to partially delegate control of the authenticated data in avenues like the healthcare industry and government organizations. We also explore using Blockchain to provide a tamper-proof and robust audit log mechanism.

cs.CR↗

arXiv2023

A large deformation model for quasi-static to high strain rate response of a rate-stiffening soft polymer

Polyborosiloxane (PBS) is an important rate-stiffening soft polymer with dynamic, reversible crosslinks used in applications ranging from self-healing sensing and actuation to body and structural protection. Its highly rate-dependent response, especially for impact-mitigating structures, is important. However, the large strain response of PBS has not been characterized over quasi-static to high strain rates. Currently, there are no constitutive models that can predict the strongly rate-dependent large-deformation elastic-viscoplastic response of PBS. To address this gap, we have developed a microstructural physics motivated constitutive model for PBS and similar soft polymers and polymer gels with dynamic crosslinks to predict their large strain, non-linear loading-unloading, and significantly rate-dependent response. We have conducted compression experiments on PBS up to true strains of $\sim$125$\%$ over a wide strain rate range of 10$^{-3}$ s$^{-1}$ to 10$^{3}$ s$^{-1}$. The model reasonably accurately captures the response of PBS over six decades of strain rates. We propose boron-oxygen coordinate-bond dynamic crosslinks with macroscopic relaxation timescale $τ\approx 3$ s and temporary entanglement lockups at high strain rates acting as crosslinks with $τ\approx 0.0005$ s as the two types of crosslink mechanisms in PBS. We have outlined a numerical update procedure to evaluate the convolution-like time integrals arising from dynamic crosslink kinetics. Experiments involving three-dimensional inhomogeneous deformations were used to verify the predictive capabilities of our model and its finite element implementation. The modeling framework can be adopted for other dynamically crosslinked rate-stiffening soft polymers and polymer gels that are microstructurally similar to PBS.

cond-mat.soft↗

arXiv2021

Modeling and prediction of COVID-19 in the United States considering population behavior and vaccination

COVID-19 has devastated the entire global community. Vaccines present an opportunity to mitigate the pandemic; however, the effect of vaccination coupled with the behavioral response of the population is not well understood. We propose a model that incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with the number of infectious cases, while an increasing sense of safety with increased vaccination lowers precautionary behaviors. To the best of our knowledge, this is the first model that can effectively reproduce the complete time history of COVID-19 infections for various regions of the United States and provides relatable measures of dynamic changes in the population behavior and disease transmission rates. We propose a parameter d_I as a direct measure of a population's caution against an infectious disease, that can be obtained from the ongoing new infectious cases. The model provides a method for quantitative measure of critical infectious disease attributes for a population including highest disease transmission rate, effective disease transmission rate, and disease related precautionary behavior. We predict future COVID-19 pandemic trends in the United States accounting for vaccine rollout and behavioral response. Although a high rate of vaccination is critical to quickly end the pandemic, we find that a return towards pre-pandemic social behavior due to increased sense of safety during vaccine deployment, can cause an alarming surge in infections. Our results indicate that at the current rate of vaccination, the new infection cases for COVID-19 in the United States will approach zero by the end of August 2021. The model can be used for predicting future epidemic and pandemic dynamics before and during vaccination.

q-bio.PE↗