*orange highlight indicates that it has already been done.
l Title
A Comprehensive Study on Data Security and Privacy Issues in E-commerce
KEYWORDS:
E-commerce, Data Security, Privacy Protection, AES (Advanced Encryption Standard), RSA (Rivest–Shamir–Adleman), ECC (Elliptic Curve Cryptography), Hash Functions, Bloom Filters, Homomorphic Encryption, Data Privacy
l Introduction
Background and Context:
1. Introduce the dynamic landscape of global e-commerce.
2. Emphasize the surging importance of data security and privacy.
3. Reference pivotal incidents like the 2023 Honda e-commerce platform attack and the Samsung data breach for urgency.
Purpose of the Study:
4. Underline the imperative for robust security across e-commerce scales.
5. Present the study’s focus on data security and privacy theories, algorithms, and technologies.
6. Highlight key technologies: advanced encryption (AES, RSA, ECC), privacy algorithms (hash functions, Bloom filters, homomorphic encryption, differential privacy), and advocate for hybrid solutions.
l Literature Review
Overview of Current State:
1. Summarize existing literature on e-commerce data security and privacy.
2. Summarize challenges and emerging trends.
Noteworthy Technologies:
3. Detail explanations of AES, RSA, ECC, hash functions, Bloom filters, and homomorphic encryption.
4. Analyze and compare these technologies in the e-commerce data security context.
5. Explore the potential of hybridizing encryption and privacy algorithms.
l Methodology
Hybrid Solution Procedure:
1. Performance Measurement of Each Algorithm:
Perform performance measurements for each encryption algorithm (AES, RSA, Homomorphic Encryption) and privacy algorithm (Hash Functions, Bloom Filters).
2. Establishment of Evaluation Criteria:
Set evaluation criteria for processing speed, resource consumption, security level, etc.
Establish criteria considering key performance metrics in an e-commerce environment.
3. Design of Hybrid Solution:
Design the integration and interaction of algorithms based on the proposed hybrid ideas.
Adjust each algorithm to collaborate organically, providing comprehensive security.
4. Data Testing:
Measure data processing, search, and analysis results to compare with existing algorithms.
Select a testing method from the mentioned methodologies (advice from the professor needed).
5. Security Analysis:
Emphasize security levels and evaluate vulnerabilities of each algorithm.
Assume scenarios like external attacks and internal data leaks, and perform security analysis.
6. Results Interpretation and Optimization:
Identify strengths and areas for improvement in the solution based on test and analysis results.
As an undergraduate researcher, conducting direct performance measurements in a real e-commerce environment can often be challenging. However, I have identified several alternative methods that could yield meaningful performance evaluations even within the constraints of undergraduate research. Here are some considerations:
1. Simulation and Modeling: It is possible to model the e-commerce environment and measure performance through simulations. This allows for virtual experiments under specific scenarios or data quantities.
2. Utilizing Open Datasets: Experimentation can be carried out using publicly available e-commerce datasets. This approach provides conditions closely resembling real-world data, contributing to more realistic performance assessments.
3. Utilizing Existing Research Results: Leveraging findings from previously conducted studies or papers allows for incorporating those results into performance evaluations within an e-commerce context. However, caution is advised, considering potential differences in environments or conditions.
4. Indirect Assessments: Theoretical evaluations of specific algorithm characteristics and understanding how algorithms perform under different circumstances can be conducted. This involves a more theoretical assessment of algorithmic effectiveness in certain situations.
+Case Study(Optional):
Pinduoduo Internship Experience:
1. Provide insights into the Pinduoduo internship.
2. Explain how this experience motivated my interest in data security and privacy protection.
l Analysis and Findings
Evaluation of Existing Methods:
1. Analyze strengths and weaknesses of current data protection methods.
2. Identify areas for improvement and propose innovations(hybrid solutions) .
Proposed Solutions:
1. Introduce hybrid solutions for evolving cybersecurity threats and privacy protection in e-commerce.
2. Explain the potential impact and feasibility.
l Conclusion
Summary of Findings:
1. Summarize key findings from the analysis.
2. Revisit the significance of data security and privacy in the expanding e-commerce landscape.
Implications and Future Work:
3. Discuss broader implications.
4. Suggest potential future research in this dynamic field.
Closing Statement:
5. Reflect on the research journey.
6. Emphasize the ongoing importance of securing data and protecting privacy in e-commerce.
7. Express gratitude for the opportunity to contribute to this critical area of study.