Homomorphic Evaluation of the Integer Arithmetic Operations for Mobile Edge Computing

Cheng, Gong; Li, Mengfei; Zhao, Liang; Guo, Zhenzhou; Han, Guangjie

doi:10.1155/2018/8142102

Cited by 28 publications

(6 citation statements)

References 36 publications

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“…The library used for DGHV homomorphic operations is DGHVlib-v1.1 [52], [53]. It has various implementations of DGHV such as DGHV itself [50], CMNT [54] and CNT [55].…”

Section: Methodsmentioning

confidence: 99%

Privacy Preserving and Serverless Homomorphic-Based Searchable Encryption as a Service (SEaaS)

Ihtesham,

Tahir,

Tahir

et al. 2023

IEEE Access

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Serverless computing has seen rapid growth, thanks to its adaptability, elasticity, and deployment agility, embraced by both cloud providers and users. However, this surge in serverless adoption has prompted a reevaluation of security concerns and thus, searchable encryption has emerged as a crucial technology. This paper explores the Searchable Encryption as a Service (SEaaS) and introduces an innovative privacy-preserving Multiple Keyword Searchable Encryption (MKSE) scheme within a serverless cloud environment, addressing previously unmet security goals. The proposed scheme employs probabilistic encryption and leverages fully homomorphic encryption to enable operations on ciphertext, facilitating searches on encrypted data. Its core innovation lies in the use of probabilistic encryption for private multi-keyword searches. To validate its practicality, we deploy the scheme on the public cloud infrastructure, "Contabo," and conduct rigorous testing on a real-world dataset. The results demonstrate that our novel scheme successfully preserves the privacy of search queries and access patterns, achieving robust security. This research contributes to the field of serverless cloud security, particularly in the context of searchable encryption, by providing a refined solution for safeguarding data while maintaining usability in a serverless computing landscape. INDEX TERMS homomorphic encryption, cloud computing, multi-keyword search, serverless computing, trapdoor generation, search pattern security I. INTRODUCTIONC LOUD computing has enabled convenient usage of computational and network resources [1]. For data storage as well as its management, the cloud provisions multiple services. The cloud is inexpensive [2] but storing data on the Cloud makes it vulnerable to advanced persistent threats (APT) [3]. Serverless computing is rapidly expanding as a result of its widespread adoption by cloud providers and tenants due to its adaptability, elasticity, and deployment agility With the boom in the serverless paradigm, security issues are being reevaluated as well [4] [5]. One way to pro-tect data from these vulnerabilities is by encrypting it prior to its storage on the cloud. However, traditional encryption techniques limit the usability of the data i.e. keyword search can not be performed on the ciphertext. The solution to this lies in searchable encryption (SE).Homomorphic Encryption (HE) is a groundbreaking approach that has enabled cloud data owners/users to not only store but also to search data [6]. HE paired with SE has opened many pathways for the cloud computing industry. Consider the healthcare industry, which has shifted towards adopting serverless cloud solutions to cater to the high vol-

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“…The library used for DGHV homomorphic operations is DGHVlib-v1.1 [52], [53]. It has various implementations of DGHV such as DGHV itself [50], CMNT [54] and CNT [55].…”

Section: Methodsmentioning

confidence: 99%

Privacy Preserving and Serverless Homomorphic-Based Searchable Encryption as a Service (SEaaS)

Ihtesham,

Tahir,

Tahir

et al. 2023

IEEE Access

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“…It was deemed essential to guarantee the security of end users and information of private and public systems such as smart grids and smart mobility. Gong et al [123] proposed a homomorphic encryption technique to guarantee transmission security and evaluate next-generation mobile edge computing. Also, Eckhoff and Wagner [59] discussed the applications areas, attackers and their data sources, privacy, enabling technologies of a smart city.…”

Section: Privacy and Security Issues In Drones And Iot Collaboramentioning

confidence: 99%

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

et al. 2019

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Smart cities contain intelligent things which can intelligently automatically and collaboratively enhance life quality, save people's lives, and act a sustainable resource ecosystem. To achieve these advanced collaborative technologies such as drones, robotics, artificial intelligence, and Internet of Things (IoT) are required to increase the smartness of smart cities by improving the connectivity, energy efficiency, and quality of services (QoS). Therefore, collaborative drones and IoT play a vital role in supporting a lot of smartcity applications such as those involved in communication, transportation, agriculture,safety and security, disaster mitigation, environmental protection, service delivery, energy saving, e-waste reduction, weather monitoring, healthcare, etc. This paper presents a survey of the potential techniques and applications of collaborative drones and IoT which have recently been proposed in order to increase the smartness of smart cities. It provides a comprehensive overview highlighting the recent and ongoing research on collaborative drone and IoT in improving the real-time application of smart cities. This survey is different from previous ones in term of breadth, scope, and focus. In particular, we focus on the new concept of collaborative drones and IoT for improving smart-city applications. This survey attempts to show how collaborative drones and IoT improve the smartness of smart cities based on data collection, privacy and security, public safety, disaster management, energy consumption and quality of life in smart cities. It mainly focuses on the measurement of the smartness of smart cities, i.e., environmental aspects, life quality, public safety, and disaster management.

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“…Additionally, our proposed method computes the normalisation and classification in the encrypted domain. Other implementations using FHE to provide security exist investigating use in mobile networks [17,18].…”

Section: Related Workmentioning

confidence: 99%

Secure signal processing using fully homomorphic encryption

Shortell

Shokoufandeh

2020

IET Information Security

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Homomorphic Evaluation of the Integer Arithmetic Operations for Mobile Edge Computing

Cited by 28 publications

References 36 publications

Privacy Preserving and Serverless Homomorphic-Based Searchable Encryption as a Service (SEaaS)

Privacy Preserving and Serverless Homomorphic-Based Searchable Encryption as a Service (SEaaS)

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

Secure signal processing using fully homomorphic encryption

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