A Framework for Homomorphic, Private Information Retrieval Protocols in the Cloud

Fahsi, Mahmoud; Benslimane, Sidi Mohamed; Rahmani, Amine

doi:10.5815/ijmecs.2015.05.03

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…For instance, having numerous distributed POS systems increases a firm's exposure to potential threats such as malware and hackers. Indeed, Fahsi et al (2015) state that "large companies' networks offer more targets for hackers." Consequently, it is expected that larger firms will have a higher likelihood of experiencing a POS-related breach compared to smaller ones.…”

Section: Factors Associated With Point-of-sale Breachesmentioning

confidence: 99%

Data breaches in hospitality: is the industry different?

Gwebu

Barrows

2020

JHTT

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Purpose The purpose of this study is to expand on the existing literature by specifically examining data security incidents within the hospitality industry, assessing origins and causes, comparing breaches within the industry with those of other industries and identifying areas of concern. Design/methodology/approach A sample of data breach incidents is drawn from the Verizon VERIS Community Database (VCDB). Statistical comparisons between hospitality and non-hospitality industry firms are conducted following the Verizon A4 threat framework. Findings The results reveal that breaches between hospitality and non-hospitality firms differ significantly in terms of actors, actions, assets and attributes. Specifically, proportions of breaches in the hospitality industry are larger in terms of external actors, hacking and malware, user devices compromised and integrity violations. Additionally, compared to other industries, point-of-sales (POS) system breaches occur at a higher rate in the hospitality industry. The study finds that company size, hacking and malware predict the likelihood of a POS breach. Research limitations/implications The study uses secondary data and does not include the entire universe of data breaches. Originality/value In the quest to reduce data breach incidents, it is imperative to identify and assess the nature of data breach incidents between industries. Doing so permits the development of targeted industry-specific solutions rather than generic ones. This study systematically identifies differences between hospitality and non-hospitality data security incidents and then suggests areas where hospitality companies should focus future attention to mitigate breach incidents.

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Section: Factors Associated With Point-of-sale Breachesmentioning

confidence: 99%

Data breaches in hospitality: is the industry different?

Gwebu

Barrows

2020

JHTT

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“…Several studies explored homomorphic encryption for different applications. For instance, Fahsi et al [28] presented a framework for homomorphic encryption and private information retrieval protocols in the cloud for the purpose of protecting users against unauthorized data access. The researchers acknowledge existence of other homomorphic encryption methods and propose an improvement for protecting users online or in the cloud.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The homomorphic techniques that have described operate as component and the processes must be completed independently. However, an encryption scheme is considered full when it consists of a plain text, a cipher text, a key space, an encryption algorithm, and a decryption algorithm [33,28,29]. According to Chen et al [43], "a fully homomorphic encryption (FHE) scheme allows arbitrary functions on certain data (referred to as plaintexts) to be performed via their ciphertexts (the encrypted version of the plaintexts) without decrypting the ciphertexts first; therefore, performing these functions does not require one to hold the secret decryption key corresponding to the encryption algorithm."…”

Section: Fully Homomorphic Encryptionmentioning

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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“…Some of these works were interested by image data such as (Newton, 2003) and (Gross, 2009) concerning de-identification of facial images using k-same model and Active Appearance Model with multi-factor model. We mention also (Du, 2011) in which the authors presented their approach of de-identification of licence plate using the least degradation methods. However, the most works of de-identification of data were the ones that are interested by the medical records such as in (Gardner, 2010) where the authors present a general framework that consists of detecting and removing identifiers using HIPAA identifiers and several methods of Anonymization.…”

Section: Automatic De-identificationmentioning

confidence: 99%

De-Identification of Health Data in Big Data using a Novel Bio-Inspired Apoptosis Algorithm

Rahmani

Amine

Hamou

2015

International Journal of Organizational and Collective Intelligence

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In the last years, with the emergence of new technologies in the image of big data, the privacy concerns had grown widely. However, big data means the dematerialization of the data. The classical security solutions are no longer efficient in this case. Nowadays, sharing the data is much easier as well as saying hello. The amount of shared data over the web keeps growing from day to another which creates a wide gap between the purpose of sharing data and the fact that these last contain sensitive information. For that, the researches turned their attention to new issues and domains in order to minimize this gap. In other way, they intended to ensure a good utility of data by preserving its meaning while hiding sensitive information to prevent identity disclosure. Many techniques had been used for that. Some of it is mathematical and other ones using data mining algorithms. This paper deals with the problem of hiding sensitive data in shared structured medical data using a new bio-inspired algorithm from the natural phenomena of apoptosis cells in human body.

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A Framework for Homomorphic, Private Information Retrieval Protocols in the Cloud

Cited by 6 publications

References 16 publications

Data breaches in hospitality: is the industry different?

Data breaches in hospitality: is the industry different?

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

De-Identification of Health Data in Big Data using a Novel Bio-Inspired Apoptosis Algorithm

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