Two Schemes of Privacy-Preserving Trust Evaluation

Yan, Zheng; Ding, Wenxiu; Niemi, Valtteri; Vasilakos, Athanasios V.

doi:10.1016/j.future.2015.11.006

Cited by 57 publications

(43 citation statements)

References 25 publications

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“…When a user requests the pre-evaluation result from EP, the EP first checks the user's access eligibility with AP. If the check is positive, the AP re-encrypts the pre evaluation result that can be decrypted by the requester (Scheme 1) or there is an additional step involving the EP that prevents the AP from obtaining the plain pre-evaluation result while still allowing decryption of the pre-evaluation result by the requester (Scheme 2) [53].…”

Section: Privacy-preserving Big Data Publishingmentioning

confidence: 99%

Big data privacy: a technological perspective and review

2016

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Section: Privacy-preserving Big Data Publishingmentioning

confidence: 99%

Big data privacy: a technological perspective and review

2016

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“…To ensure maximum scalability, they implemented a hybrid of the OpenMP-MPL programming paradigm, by means of which they enabled each processing core to be assigned a number of files and then for each core to subdivide these files, depending on the number of threads being used [33]. Yan et al (2016) propose two security schemes in which the aim is to protect the confidential information of trusted suppliers. The first scheme focuses on computational efficiency while the second provides better protection at the expense of computational cost.…”

Section: Security Mechanisms Based Literature Reviewmentioning

confidence: 99%

“…The first scheme focuses on computational efficiency while the second provides better protection at the expense of computational cost. They use proxy-based additive homomorphism with reencryption to design these two schemes for Privacy-Preserving Trust Evaluation (PPTE) [34]. Zhou et al (2015) propose an encryption algorithm that focuses on image security.…”

Section: Security Mechanisms Based Literature Reviewmentioning

confidence: 99%

A Systematic Review of Security Mechanisms for Big Data in Health and New Alternatives for Hospitals

Hamrioui

Díez

Garcia‐Zapirain

et al. 2017

Wireless Communications and Mobile Computing

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Computer security is something that brings to mind the greatest developers and companies who wish to protect their data. Major steps forward are being taken via advances made in the security of technology. The main purpose of this paper is to provide a view of different mechanisms and algorithms used to ensure big data security and to theoretically put forward an improvement in the health-based environment using a proposed model as reference. A search was conducted for information from scientific databases as Google Scholar, IEEE Xplore, Science Direct, Web of Science, and Scopus to find information related to security in big data. The search criteria used were "big data", "health", "cloud", and "security", with dates being confined to the period from 2008 to the present time. After analyzing the different solutions, two security alternatives are proposed combining different techniques analyzed in the state of the art, with a view to providing existing information on the big data over cloud with maximum security in different hospitals located in the province of Valladolid, Spain. New mechanisms and algorithms help to create a more secure environment, although it is necessary to continue developing new and better ones to make things increasingly difficult for cybercriminals.

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“…Wei et al [6] proposed a secure computation auditing protocol which bridges secure storage and computation within a cloud and achieves privacy using verifier signature, batch verification, and probabilistic sampling techniques. Yan et al [7] proposed two privacy preserving techniques for trust evaluation based on additive homomorphic encryption. It is applicative approach and supports big data process.…”

Section: Related Workmentioning

confidence: 99%

Scalable two-phase co-occurring sensitive pattern hiding using MapReduce

Sharma

Toshniwal

2017

J Big Data

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Background: Expansion of Internet and its use for on-line activities such as E-Commerce and social networking are producing large volumes of transactional data. This huge data volume resulted from these activities facilitates the analysis and understanding of global trends and interesting patterns used for several decisive purposes. Analytics involved in these processes expose sensitive information present in these datasets, which is a serious privacy threat. To overcome this challenge, few sequential heuristics have been used in past where volumes of data were comparatively accommodating to these sequential heuristics; the current situation is not that much in-line and often results in high execution time. This new challenge of scalability paves a way for experimenting with Big Data approaches (e.g., MapReduce Framework). We have agglomerated the MapReduce framework with adopted heuristics to overcome this challenge of scalability along with much-needed privacy preservation and yields efficient analytic results within bounded execution times. Methods:MapReduce is a parallel programming framework [16] which provides us the opportunity to leverage largely distributed resources to deal with the Big Data analytics. MapReduce allows the resource of a largely distributed system to be utilized in a parallel fashion. The simplicity and high fault-tolerance are the key features which make MapReduce a promising framework. Therefore, we have proposed a two-phase MapReduce version of these adopted heuristics. MapReduce framework divides the whole data into 'n' number of data chunks D = {d 1 d ∪ 2 ∪ d 3 ..... ∪ d n } and distributes them over 'n' computing nodes to achieve the parallelization. The first phase of MapReduce job runs on each data chunk in order to generate intermediate results, which are further sorted and merged in the second phase to generate final sanitized dataset. Results:We conducted three set of experiments, each with five different scenarios corresponding to the different cluster sizes i.e., n = 1,2,3,4,5 where 'n' is a number of computing nodes. We compared the approaches with respect to real as well as synthetically generated large datasets. For varying data sizes and varying number of computing nodes, it has been observed that sanitization time required by the MapReduce-based algorithm for same size dataset is much less than the sequential traditional approach. Further, the scalability can be improved by using more number of computing nodes. Lastly, another set of experiments explores the change in sanitization time with varying sizes of the sensitive content present in a dataset. We evaluated the effectiveness of proposed approach in different scenarios, with varying cluster size from 1 to 5 nodes. It has been observed that still the execution time of our approach is much less than traditional schemes. Further, no hiding failure, artifactual patterns Sharma and Toshniwal J Big Data (2017) 4:4 DOI 10.1186/s40537-017-0064-9 Page 2 of 18 Sharma and Toshniwal J Big Data (2017) 4:4 ha...

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Two Schemes of Privacy-Preserving Trust Evaluation

Cited by 57 publications

References 25 publications

Big data privacy: a technological perspective and review

Big data privacy: a technological perspective and review

A Systematic Review of Security Mechanisms for Big Data in Health and New Alternatives for Hospitals

Scalable two-phase co-occurring sensitive pattern hiding using MapReduce

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