Big Data Privacy Using Fully Homomorphic Non-Deterministic Encryption

Patil, Tejashree B.; Patnaik, Girish Kumar; Bhole, Ashish T.

doi:10.1109/iacc.2017.0041

Cited by 8 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various big data topics and applications have received attention over the past few years and security concerns have been given priority in big data research [1]. Most of the security e orts are geared towards cloud based big data platforms.…”

Section: Motivation and Scopementioning

confidence: 99%

“…The other area that has received more attention regarding security and the prospect of using homomorphism is cloud computing, especially in respect to di erent healthcare application and data storage systems [51]. Patil et al [1] proposed an arithmetic based homomorphism that can to protect cyber space and promote secure sharing of information in different communication settings. However, the proposed system relies on FHE and ElGamal cryptosystems to encrypt the data although on a system [50].…”

Section: Big Data and Cloud Computingmentioning

confidence: 99%

“…The advent of big data and cloud computing among some of the emerging industry 4.0 concepts have brought with them innumerable security concerns. According to Patil et al [1], big data refers to the large volume of data collected from the Internet, social network, and smart phones as well as other sources that generate huge volumes of data. Besides the large and complex nature of big data, the other notable characteristics include volume, variety, and velocity [1].…”

Section: Introductionmentioning

confidence: 99%

“…According to Patil et al [1], big data refers to the large volume of data collected from the Internet, social network, and smart phones as well as other sources that generate huge volumes of data. Besides the large and complex nature of big data, the other notable characteristics include volume, variety, and velocity [1]. Kumar et al [2] identified additional dimensions to big data; value and veracity [2].…”

Section: Introductionmentioning

confidence: 99%

“…Kumar et al [2] identified additional dimensions to big data; value and veracity [2]. Volume, in the context of big data, refers the numbers of records, transactions, and tables presented within the data set as measured in terabytes, while velocity refers to real-time processing of the data in batches using streams and associated technical performance [1,2,3]. Furthermore, value of big data refers to statistical inferences, events within the data sets, correlations among attributes, and hypotheses used in the analysis, while veracity attributes to the trustworthiness, authenticity, accountability, and availability of the data generated by the systems [2].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Motivation and Scopementioning

confidence: 99%