A faster fully homomorphic encryption scheme in big data

Wang, Dan; Guo, Bing; Shen, Yan; Cheng, Shun-Jun; Lin, Yonghong

doi:10.1109/icbda.2017.8078836

Cited by 14 publications

(4 citation statements)

References 9 publications

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“…Concerning securing BD, an improved FHE-DGHV scheme with shorter public key size was proposed by Wang et al [17]. Considering the error-free approximate GCD problem, they claimed that the novel scheme was semantically secure.…”

Section: B Access Control and Encryption Techniquesmentioning

confidence: 99%

Securing Big Data: A Survey on Security Solutions

Elshall¹

2022

Preprint

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Section: B Access Control and Encryption Techniquesmentioning

confidence: 99%

Securing Big Data: A Survey on Security Solutions

Elshall¹

2022

Preprint

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“…The other emerging application of homomorphic encryption is in the field of big data and cloud computing.Chakraborty and K Patra [49] investigated the use of functional encryption in big data analytics and concluded that functional encryption algorithms that create public keys and secret masters keys have an added advantage because all the generated keys are based the master key and the computation involved in decrypting the message is dependent on the value of the generated key [32]. Liu et al [50] and other authors Dijk and Gentry [40], Jie and Jing [51], Wang et al [52], Tsoutsos and Maniatakos [53], Geetanjali and Sambhaji [54], Chou et al [55] also discussed the application of homomorphic encryption in online systems and proposed di erent points of integrating the encryptions within the big data systems. Besides, the Paillier and ElGamal cryptosystems [56], Menandas and Joshi [57] postulated that Okamoto Uchiyama Cryptosystem, despite its complexity, is more suited for big data systems with sensitive information.…”

Section: Big Data and Cloud Computingmentioning

confidence: 99%

A systematic review on the status and progress of homomorphic encryption technologies

Alloghani

Alani

Al‐Jumeily

et al. 2019

Journal of Information Security and Applications

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With the emergence of big data and the continued growth in cloud computing applications, serious security and privacy concerns emerged. Consequently, several researchers and cybersecurity experts have embarked on a quest to extend data encryption to big data systems and cloud computing applications. As most cloud users turn to using public cloud services, confidentiality becomes and even more complicated issue. Cloud clients storing their data on a public cloud always seek solutions to confidentiality problem. Homomorphic encryption emerged as a possible solution where client's data is encrypted on the cloud in a way that allows some search and manipulation operations without proper decryption. In this paper, we present a systematic review of research paper published in the field of homomorphic encryption. This paper uses PRISMA checklist alongside some items of Cochrane's Quality Assessment to review studies retrieved from various resources. It was highly noticeable in the reviewed papers that security in big data and cloud computing has received most attention. Most papers suggested the use of homomorphic encryption although the thematic analysis has identified other potential concerns. Regarding the quality of the articles, 38% of the articles failed to meet three checklist items, including explicit statement of research objectives, procedure recognition and sources of funding used in the study. The review also presented compendium textual analysis of di erent homomorphic encryption algorithms, application areas, and areas of future developments. Results of the evaluation through PRISMA and the Cochrane tool showed that a majority of research articles discussed the potential use and application of Homomorphic Encryption as a solution to the growing demands of big data and absence of security and privacy mechanisms therein. This was evident from 26 of the total 59 articles that met the inclusion criteria. The term Homomorphic Encryption appeared 1802 times in the word cloud derived from the selected articles, which speaks of its potential to ensure security and privacy, while also preserving the CIA triad in the context of big data and cloud computing.

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“…Another related problem is that if an adversary is able to observe the analytic operations or data storage read/write requests, sensitive information might be leaked. One way to tackle this problem is to use privacy-preserving cryptographic techniques [4] [5], [6]. However, so far these approaches have large overhead and thus severely limits the type of analytic operations that can be supported in the system.…”

Section: Introductionmentioning

confidence: 99%

An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics

Gehrmann

Gunnarsson

2019

2019 IEEE International Conference on Big Data (Big Data)

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Efficient protection of huge amount of IoT produced data is key for wide scale data analytic services. The most efficient way is to use pure symmetric encryption as that allows both fast decryption at the analytic engine side as well as energy efficient encryption at the IoT side. However, symmetric encryption can only be performed if there is a way to directly map an encrypted object to the correct key. Typically, such mapping require a unique IoT identity, which constitute a privacy problem. In this paper, we present an IoT identity protection scheme for symmetric IoT data encryption. We give basic security definitions for this problem setting, present a new construction and give security proofs of security level achieved with the construction. Performance figures for a proof of concept implementation are also given. The new scheme gives a fair trade-off between identity privacy and complexity.

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A faster fully homomorphic encryption scheme in big data

Cited by 14 publications

References 9 publications

Securing Big Data: A Survey on Security Solutions

Securing Big Data: A Survey on Security Solutions

A systematic review on the status and progress of homomorphic encryption technologies

An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics

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