A Scalable Implementation of Fully Homomorphic Encryption Built on NTRU

Rohloff, Kurt; Cousins, David Bruce

doi:10.1007/978-3-662-44774-1_18

Cited by 32 publications

(26 citation statements)

References 14 publications

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“…This is one of the major cloud issue which should be considered at every phase of designing a cloud architecture (Wang et al 2009;Kamara and Lauter 2010;Mansfield-Devine 2012;Asghar et al 2013;Wang et al 2014a;TCG 2015;Herranz et al 2014;Wang et al 2014b;Coron et al 2014;Smart and Vercauteren 2014;Wang et al 2015a;Halevi and Shoup 2015a;Fujinoki 2015;Hadavi et al 2015;Demmler et al 2015;Roy et al 2015;AlBelooshi et al 2015;Han et al 2015;Xiang et al 2016). There are many types of sensitive information: personal, corporate/governmental financial information, health, biometric information, religious or racial information, sexual orientation, job performance information, collections of surveillance, taking photos/videos in public places, usage data collected from various devices (computers, printers, smart phones), behavioral information (e.g., viewing digital content), visited websites, product usage history, IP addresses, RFID tags, and unique hardware identities.…”

Section: Privacymentioning

confidence: 96%

“…The CSP does not see what has happened. Although there are significant improvements the existing implementations are unfortunately not yet to be practical in the cloud setting (Rohloff and Cousins 2014;Halevi and Shoup 2015a).…”

Section: Partially Somewhat and Fully Homomorphic Encryption Schemesmentioning

confidence: 98%

“…Virtual machines (VM) are the key aspects of cloud computing where different users (called tenants) access the same physical hardware. However, a corrupted hypervisor can damage all systems that it hosts (Kandukuri et al 2009;Ideler 2012;DNSP 2014;ES 2014;Opara-Martins et al 2015;Klemperer 2015;AlBelooshi et al 2015;Roy et al 2015). One of the best proposed solutions is to utilize Trusted Platform Modules (TPM) which can provide hardware-based verification of hypervisor and VM integrity in order to guarantee network separation and security.…”

Section: Virtualization Hypervisors and Multi-tenancymentioning

confidence: 98%

“…Access Management includes the authorization of access only to the data that an entity needs to access, and to perform required duties efficiently and effectively Tep et al 2015;Roy et al 2015). While authorization determines the user's right to access a certain type of resource, access management in general has the responsibility to enforce the users' accessing to a given resource, and managed with respect to the users credentials and attributes associated with the identity policies.…”

Section: Authorization and Access Managementmentioning

confidence: 99%

“…Wang et al 2015a; Halevi and Shoup 2015a;Fujinoki 2015;Hadavi et al 2015;Demmler et al 2015;Roy et al 2015;AlBelooshi et al 2015; Han et al 2015;Xiang et al 2016) Virtualization (Rootkit in hypervisor and VM escape)…”

mentioning

confidence: 98%

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A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

Kiraz

2016

J Ambient Intell Human Comput

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The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today's real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view.

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Section: Privacymentioning

confidence: 96%

Section: Partially Somewhat and Fully Homomorphic Encryption Schemesmentioning

confidence: 98%

Section: Virtualization Hypervisors and Multi-tenancymentioning

confidence: 98%

Section: Authorization and Access Managementmentioning

confidence: 99%

mentioning

confidence: 98%

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A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

Kiraz

2016

J Ambient Intell Human Comput

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A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

Pulido-Gaytán

Tchernykh

Cortés-Mendoza

et al. 2021

Communications in Computer and Information Science

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Application of Homomorphic Encryption in Machine Learning

Ameur

Bouzefrane

Audigier

2022

Emerging Trends in Cybersecurity Applications

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Big data technologies, such as machine learning, have increased data utility exponentially. At the same time, the cloud has made the deployment of these technologies more accessible. However, computations of unencrypted sensitive data in a cloud environment may expose threats and Cybersecurity attacks. We consider a class of innovative cryptographic techniques called Privacy-Preserving Technologies (PPTs) to address this problem. That might help increase utility by taking more significant advantage of the cloud and machine learning technologies while preserving privacy.The first section provides a brief introduction to the so-called Homomorphic Encryption "HE" by giving an overview of the most promising schemes and then giving the current state of the art of HE tools such as Libraries and Compilers. This section aims to help non-cryptographer developers propose HE solutions by explaining what makes developing HE applications challenging.Then, we address the Privacy-Preserving in Machine Learning (PPML), an approach that allows to train and deploy ML models without exposing their private data. After exploring state-of-the-art for the most used ML models in PPML, we will overview applications of Homomorphic Encryption in Machine Learning.

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A Scalable Implementation of Fully Homomorphic Encryption Built on NTRU

Cited by 32 publications

References 14 publications

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

Application of Homomorphic Encryption in Machine Learning

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