A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

El-Yahyaoui, Ahmed; Kettani, Mohamed Dâfir Ech-Cherif El

doi:10.3390/technologies7010021

Cited by 21 publications

(5 citation statements)

References 2 publications

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“…This paper is an extension of our prior works [1], [2], [3]. In these works, we proposed improved cryptographic methods for searching and computation over encrypted data.…”

Section: Contributionmentioning

confidence: 89%

“…a noise part is added to cleartexts in order to obtain the homomorphic properties. Most of the work that followed later has inspired from Gentry's framework to design FHE cryptosystems [34], [35], [36], [37], [38], [39],… other ideas and frameworks appeared later [40], [41], [42], [1], [2]… Among the most prominent attempts to simplify homomorphic encryption schemes is the cryptosystem MORE [41]. This latter was the subject of the patent WO2014016795A2.…”

Section: Communication Channels Attacksmentioning

confidence: 99%

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An improved Framework for Biometric Database’s privacy

El-Yahyaoui¹,

OMARY²

2021

Int. j. commun. netw. inf. secur.

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Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language.

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“…This paper is an extension of our prior works [1], [2], [3]. In these works, we proposed improved cryptographic methods for searching and computation over encrypted data.…”

Section: Contributionmentioning

confidence: 89%

Section: Communication Channels Attacksmentioning

confidence: 99%

An improved Framework for Biometric Database’s privacy

El-Yahyaoui¹,

OMARY²

2021

Int. j. commun. netw. inf. secur.

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“…Garcia and Jacobs (2010) and Gomathisankaran et al (2013) adopted homomorphic encryption (HE). Sensitive data are processed encrypted and there is no information leakage in the process as shown in Figure 6 (El-Yahyaoui and Ech-Cherif El Kettani, 2019). There is an approach to encrypt sensitive areas in images or videos as shown in Figure 7 (Chattopadhyay and Boult, 2007).…”

Section: Privacy-preserving Approaches To Human and Human Activity Recognitionmentioning

confidence: 99%

A review of privacy-preserving human and human activity recognition

Jung

2020

International Journal on Smart Sensing and Intelligent Systems

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Many automation technologies using software are making humans convenient. One of these technologies is to collect data through cameras and sensors that are common in personal life and automatically recognize human and human activities. The goal of automation is to analyze the various types of big data that are difficult to perform mechanical data mining. Raw data collected from cameras and sensors are nothing but big data before analysis. In this case, how to protect data by secure storage is the most important issue. However, when the context-aware semantic information such as a specific person and his behavior is extracted from the analysis, the security sensitivity is increased. In other words, the secondary information generated by interpreting and extracting personal location and behavioral information contained in images and videos is linked to other personal information, causing privacy infringement issues. Privacy issues become important because there is a lot of software that everyone can access. Therefore, it is necessary to study privacy protection methods in the automatic recognition of human and human activities. This paper analyzes the cutting-edge research trends, techniques, and issues of privacy-preserving human and human activity recognition.

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“…As mentioned before, the verifiability of computational results is an important security guarantee for many outsourced schemes (e.g., [173], [312], and [318]), which checks the correctness of remote computations. In scheme [173], a verification mechanism for matrix multiplication is designed by comparing the values between A(Bv) and Cv at the client (the column vector v is randomly generated by the client).…”

Section: B Verifiabilitymentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Cited by 21 publications

References 2 publications

An improved Framework for Biometric Database’s privacy

An improved Framework for Biometric Database’s privacy

A review of privacy-preserving human and human activity recognition

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

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