A Privacy-Preserving and Verifiable Statistical Analysis Scheme for an E-Commerce Platform

Shen, Hua; Wu, Ge; Xia, Zhe; Susilo, Willy; Zhang, Mingwu

doi:10.1109/tifs.2023.3269669

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…The study suggested a threshold secret exchange system with a verified threshold homomorphic encryption approach to create a secure and provable statistical analysis strategy for an e-commerce platform. By using a unique distribution model to provide secret shares, our method reduced the need for secure channels by approximately 40% when compared to a typical criterion privacy sharing scheme [10].…”

Section: Introductionmentioning

confidence: 99%

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Jia

2024

HighTech. Innov. J.

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Objectives: Consumer data privacy on e-commerce platforms is increasingly crucial. This study aims to investigate privacy protection mechanisms, particularly focusing on personal and corporate secrets. It seeks to understand individual perspectives on privacy and preferences for data disclosure. The primary objective is to explore methods for safeguarding personal information while maintaining data integrity. Methods/Analysis: We employ non-parametric statistical techniques to analyze consumer behavior and preferences on e-commerce platforms. This involves examining patterns of data disclosure and identifying sensitive information shared by users. By studying communication dynamics and recording practices, we assess the efficacy of current privacy protection measures. Novelty/Improvement: This study contributes to the understanding of consumer privacy protection by emphasizing the importance of non-parametric statistical methods in e-commerce research. Our findings underscore the need for enhanced privacy measures. We advocate for further research and development of innovative privacy-enhancing technologies to address evolving privacy challenges in online commerce. Findings:Our research highlights the significance of personal privacy concerns in e-commerce settings. We identify a spectrum of privacy attitudes among users, ranging from strict confidentiality to selective disclosure. Furthermore, our analysis reveals potential vulnerabilities in current privacy safeguards, particularly regarding the collection and storage of sensitive data on e-commerce platforms. Doi: 10.28991/HIJ-2024-05-02-013 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Jia

2024

HighTech. Innov. J.

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“…To cater to these requirements, it has become common practice to outsource individual or enterprise data to cloud servers [3]. However, the need for more control over outsourced data poses significant challenges to the privacy and security of data owners [4], [5]. Uploading encrypted data is a common solution.…”

Section: Introductionmentioning

confidence: 99%

Multi-Keywords Searchable Attribute-Based Encryption With Verification and Attribute Revocation Over Cloud Data

Shen,

Zhou,

et al. 2023

IEEE Access

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The convenience and efficient management of cloud servers has resulted in an increasing number of users opting to store their data in the cloud. Consequently, data-outsourcing services relying on cloud servers have been extensively deployed and utilized. In this case, before outsourcing the data to cloud storage, we should ensure unauthorized users cannot access the data. In this article, we present a scheme termed MKSABE-VaAR (multi-keyword searchable attribute-based encryption with verification and attribute revocation). Aiming at the problems of inefficiency, excessive computation in the search process, and only supporting single-keyword search in most attribute-based searchable encryption schemes, first, we package multiple keywords into a polynomial to realize multi-keyword search. Based on this polynomial, MKSABE-VaAR can reduce the amount of search calculation for keyword ciphertext and improve search efficiency by reducing the number of bilinear pairing operations. At the same time, we have made some special constructs for indexing to add verification of user attributes in the keyword search process, which improves the search accuracy. Moreover, we adopt a linear secret-sharing technique to construct the attribute revocation function of MKSABE-VaAR, making a lower computational cost of attribute revocation. Furthermore, the mechanism of storing data and indexes separately greatly reduces the risk of data leakage of MKSABE-VaAR.

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To secure an e‐commerce system using epidemic mathematical modeling with neural network

Yadav,

Keshri

2024

Concurrency and Computation

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SummarySecuring an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.

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A Privacy-Preserving and Verifiable Statistical Analysis Scheme for an E-Commerce Platform

Cited by 10 publications

References 41 publications

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Multi-Keywords Searchable Attribute-Based Encryption With Verification and Attribute Revocation Over Cloud Data

To secure an e‐commerce system using epidemic mathematical modeling with neural network

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