Secure Health Monitoring in the Cloud Using Homomorphic Encryption

Ames, Scott; Venkitasubramaniam, Muthuramakrishnan; Page, Alex; Kocabaş, Övünç; Soyata, Tolga

doi:10.4018/978-1-4666-8662-5.ch004

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…As such, HE represents a promising tool to greatly enhance data privacy and security, especially when outsourcing computations to the public cloud. In the meantime, HE can be extremely computationally intensive [14], and improving its computation efficiency is key to making this technology practical for real applications.…”

Section: Homomorphic Encryption (He)mentioning

confidence: 99%

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Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption

Gao,

Gang,

Homsi

et al. 2024

Preprint

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Despite the enormous technical and financial advantages of cloud computing, security and privacy have always been the primary concerns for adopting cloud computing facilities, especially for government agencies and commercial sectors with high-security requirements. Homomorphic Encryption (HE) has recently emerged as an effective tool in ensuring privacy and security for sensitive applications by allowing computing on encrypted data. One major obstacle to employing HE-based computation, however, is its excessive computational cost, which can be orders of magnitude higher than its counterpart based on the plaintext. In this paper, we study the problem of how to reduce the HE-based computational cost for general Matrix Multiplication (MM), i.e., a fundamental building block for numerous practical applications, by taking advantage of the Single Instruction Multiple Data (SIMD) operations supported by HE schemes. Specifically, we develop a novel element-wise algorithm for general matrix multiplication, based on which we propose two HE-based General Matrix Multiplication (HEGMM) Approved for Public Release on 06 Mar 2024. Distribution is Unlimited. Case Number: 2024-0184 (original case number(s): AFRL-2024-0944) algorithms to reduce the HE computation cost. Our experimental results show that our algorithms can significantly outperform the state-of-the-art approaches of HE-based matrix multiplication.

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Section: Homomorphic Encryption (He)mentioning

confidence: 99%

“…for each i ∈ [pm, (p+1)m). j −i has the same constant value in each i ∈ [pm, (p+1)m) and this means U ω k m×n has only 2 non-zero diagonals when m = l. When applying ω transformation on matrix B l×n in row-major order, we can formulate permutation matrix according to formula (14), but apply on B n×l instead of B m×l . Note that U…”

Section: Fundingmentioning

confidence: 99%

Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption

Gao,

Gang,

Homsi

et al. 2024

Preprint

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show abstract

“…An overall innovative wellbeing application region is human advance action acknowledgment, which is significant for seeing every individual's muscular strength, for the most part, the senior ones. The authors in Reference [13] built up a strategy to distinguish human advance action by including more than 40 inserted sensors, a whirligig, accelerometers, and a gauss meter in a brace-like component. Sensors assist with the computation of step tally, swing time, moving speed, and pressing factor.…”

Section: E Smart Healthmentioning

confidence: 99%

Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity

Yaswanth,

Babu,

Vineetha

et al. 2023

IJRITCC

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The goal of smart city development is to improve the quality of life by incorporating technology into daily activities. Artificial intelligence (AI) is critical to the ongoing development of future smart cities. The Internet of Things (IoT) idea connects every internet-enabled device for improved access and control. AI in various domains has changed ordinary towns into highly equipped smart cities. Machine learning and deep learning algorithms have proven indispensable in a variety of industries, and they are now being implemented into smart city concepts to automate and improve urban activities and operations on a large scale. IoT and machine learning technology are frequently used in smart cities to collect data from various sources. This article delves deeply into the significance, scope, and developments of AI-based smart cities. It also addresses some of the difficulties and restrictions associated with smart cities powered by AI. The goal of the study is to inspire and encourage academics to create original smart city solutions based on AI technologies.

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“…Privacy and security of the medical information are major concerns for Smart Health. Studies in References [8,73] aim at formulating a medical cloud computing environment for cardiac health 41:9 monitoring, in which the medical data are transmitted from the sensing plane to the data plane in a privacy-preserving fashion.…”

Section: Smart Healthmentioning

confidence: 99%

Smart City System Design

et al. 2019

Self Cite

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Recent global smart city efforts resemble the establishment of electricity networks when electricity was first invented, which meant the start of a new era to sell electricity as a utility. A century later, in the smart era, the network to deliver services goes far beyond a single entity like electricity. Supplemented by a wellestablished Internet infrastructure that can run an endless number of applications, abundant processing and storage capabilities of clouds, resilient edge computing, and sophisticated data analysis like machine learning and deep learning, an already-booming Internet of Things movement makes this new era far more exciting. In this article, we present a multi-faceted survey of machine intelligence in modern implementations. We partition smart city infrastructure into application, sensing, communication, security, and data planes and put an emphasis on the data plane as the mainstay of computing and data storage. We investigate (i) a centralized and distributed implementation of data plane's physical infrastructure and (ii) a complementary application of data analytics, machine learning, deep learning, and data visualization to implement robust machine intelligence in a smart city software core. We finalize our article with pointers to open issues and challenges.

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Secure Health Monitoring in the Cloud Using Homomorphic Encryption

Cited by 12 publications

References 35 publications

Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption

Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption

Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity

Smart City System Design

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