Improved Privacy Preservation Framework for Cloud-Based Internet of Things

Yuvaraj, N.; Raja, R. Arshath; Karthikeyan, T.; Kousik, N.V.

doi:10.1201/9781003032441-11

Cited by 11 publications

(5 citation statements)

References 1 publication

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“…Furthermore, in the [ 73 ], which also applies HE to the transfer of data from the IoT to cloud servers, and the algorithm acts as outsourced storage and computation for PP in the cloud-IoT model (Q1). Simulation results confirm that data transferred between IoT devices and the cloud is more private than existing methods.…”

Section: Literature Review and Discussionmentioning

confidence: 99%

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Lei

Liu

et al. 2022

Foods

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Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made.

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Section: Literature Review and Discussionmentioning

confidence: 99%

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Lei

Liu

et al. 2022

Foods

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“…The analysis basically deals with classifying weather The question of whether or not a person is eligible for a loan implies that the problem is one of classification, which may be solved using classification techniques [17]- [23]. The present system, CIBIL, is a credit rating system that gathers and retains records of payments made on loans and credit cards by people and businesses.…”

Section: Existing Workmentioning

confidence: 99%

Loan Eligibility Prediction using Data Science Algorithms A Comparative Analysis

Ramkumar,

Johny,

Darak

et al. 2022

JEMM

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Loan is a amount that is provided to someone else in exchange for repayment of the loan principle amount plus interest. The different variety of loans is Personal loan, Home loan, Education loan, etc. Here, Decision Tree and Naïve Bayes algorithms are used to predict the loan eligibility of a particular individual. Data mining techniques are used to extract data and predict the loan eligibility. By providing loan, the banks help in the overall flow of the economy of the country. The borrower can avail the loan whenever required and the bank can get back the principle amount with interest based on some pre-defined percentages.

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“…While physical "Credit card fraud" is possible, the regularity with which account information is stolen electronically has increased dramatically thanks to modern technologies. Until the card information is used to make transactions, the account holder, the merchant from whom the card information was stolen or intercepted, and even the card issuer may be fully unaware of the breach [26]- [34]. Because of the growing popularity of online shopping and bill payment, having a physical credit card or debit card is no longer required to make transactions or pay bills.…”

Section: "Credit Card Fraud"mentioning

confidence: 99%

“Credit Card Fraud” Detection Using Data Analytics A Comparative Analysis

Ramkumar,

Supriya,

Chaithanya

et al. 2022

JEMM

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Fraud events take place frequently which results in a huge financial loss. Fraud detections are dynamic and are not easy to identity. Data mining plays a vital role in detection of "Credit card fraud" done in fraudulent online transactions. Fraudsters use latest advanced methods which is an advantage. This process becomes challenging based on two major reasons -firstly, the profiles of users keep changing constantly and secondly, the datasets required for this are highly confusing. The overall performance of "Credit card fraud" detections is improved by sampling approach on the dataset. This research looks at fraud incidents in the context of real-life fraud transactions. A variety of machine learning methods and modelling approaches are used to combat each fraud. The goal of this study is to see how well logistic regression and K-nearest neighbour (KNN) perform on highly skewed "Credit card fraud" data. In order to assess the algorithm's robustness even further, noise is injected into the data sets. The major purpose of this study is to compare and contrast numerous methods for identifying fraud.

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Improved Privacy Preservation Framework for Cloud-Based Internet of Things

Cited by 11 publications

References 1 publication

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Loan Eligibility Prediction using Data Science Algorithms A Comparative Analysis

“Credit Card Fraud” Detection Using Data Analytics A Comparative Analysis

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