The process of developing a research proposal in the domain of cloud computing is examined as challenging as well as fascinating. Check out some of the Research Proposal topics we’ve assisted scholars with in Cloud Computing Security. We offer an instance research proposal that assist you to develop a proposal in a proper and efficient format:
Research Proposal: Enhancing Cloud Computing Security through Advanced Encryption and Access Control Mechanisms
- Introduction
By providing adaptable and scalable sources over the internet, cloud computing has become crucial to advanced IT architecture. Yet, by means of major safety problems, such as loss of data, illicit access, and violation of data, its implementation is hindered. The main intention of this research proposal is to investigate progressive encryption approaches and access control technologies in order to improve cloud computing protection.
- Goals
- Develop and evaluate advanced encryption techniques: To secure data at inactive state, during transmission, and at the time of processing, concentrate on fully homomorphic encryption and homomorphic encryption.
- Implement robust access control mechanisms: It is approachable to examine and improve Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) frameworks.
- Integrate blockchain technology for security: Specifically, for permanent logging and safe data sharing, aim to make use of blockchain.
- Evaluate the performance and security of the proposed solutions: Generally, experimental exploration and simulations has to be carried out to evaluate its performance.
- Literature Survey
3.1. Cloud Computing Security Challenges
Numerous safety limitations are confronted by cloud computing:
- Data Breaches and Unauthorized Access: Frequently, because of inadequate access control and weak authentication, violations of data are examined as a major challenge based on Ponemon Institute’s “Cost of a Data Breach Report 2020”.
- Data Integrity: Throughout the accumulation and transmission process, it is significant to verify the data whether it remains unchanged. The requirement for integrity verification technologies in cloud storage are emphasized in the study by Wang et al. (2010).
- Privacy Concerns: The confidentiality problems occurring from data co-location and multi-tenancy could be highlighted in the research by Kandukuri et al. (2009).
3.2. Encryption Approaches
- Advanced Encryption Standard (AES): Even though AES does not assist the computation of encrypted data, in the case of its resilience and potential (Daemen and Rijmen, 2002), it is broadly deployed for data security.
- Homomorphic Encryption: The fully homomorphic encryption initiated by Gentry (2009), which is capable of facilitating computation on encrypted data without decryption. Typically, this approach assures improved confidentiality, even though it is computationally intensive.
- Partially Homomorphic Encryption: Certain processes such as multiplication/addition on ciphertexts are assisted by plans such as Paillier (1999), which are least computationally challenging when compared to fully homomorphic encryption.
3.3. Access Control Mechanisms
- Role-Based Access Control (RBAC): On the basis of roles, model access to authorized users are constrained by RBAC which is initiated by Ferraiolo and Kuhn (1992). The adaptability is inadequate but it is able to condense management.
- Attribute-Based Access Control (ABAC): For providing more adaptability and granularity than RBAC, Hu et al. (2015) converse ABAC, that employs variables of users to specify access policies.
3.4. Blockchain Technology for Safety
- Immutable Logging: The efficiency of blockchain for developing immutable records, improving belief and clearness are emphasized in study by Zheng et al. (2017).
- Secure Data Sharing: The usage of blockchain in safe data sharing is indicated by Xu et al. (2019), thereby assuring data integrity and monitorability.
- Proposed Methodology
4.1. Advanced Encryption Approaches
- Implement Homomorphic Encryption:
- To deploy homomorphic encryption, it is appreciable to employ libraries such as IBM HELib or Microsoft SEAL.
- The safety advantages and performance overhead have to be assessed.
- Integrate Encryption with Cloud Services:
- In order to combine homomorphic encryption with cloud storage and computing services, construct suitable models.
- Focus on carrying out safety evaluations and performance benchmarks.
4.2. Access Control Mechanisms
- Enhance ABAC:
- To adjust on the basis of setting such as time, location, aim to create dynamic strategies.
- Through the utilization of cloud access cloud services like Azure AD, AWS IAM, it is better to deploy a model.
- Combine RBAC and ABAC:
- A hybrid framework has to be suggested by employing the adaptability of ABAC and the clarity of RBAC.
- Typically, in simulated cloud platforms, focus on assessing the framework.
4.3. Blockchain Integration
- Immutable Logging:
- For cloud processes, it is approachable to utilize blockchain-related logging.
- The protection and effectiveness of the logging framework has to be evaluated.
- Secure Data Sharing:
- Aim to construct a blockchain-related data sharing protocol.
- Generally, concentrate on assessing its performance in assuring data integrity and monitorability.
- Evaluation and Testing
- Performance Testing: To evaluate the overhead produced by encryption and access control technologies, it is beneficial to employ benchmarks and actual-world settings.
- Security Analysis: Normally, risk evaluation and penetration assessment have to be carried out to assess the effectiveness of the suggested approaches.
- Usability Testing: From an end-user aspect, evaluate the flexibility and utility of the safety technologies.
- Anticipated Contributions
- Enhanced Security: For cloud computing platforms, aim to enhance encryption and access control technologies.
- Practical Implementations: Generally, cloud service suppliers could implement models and systems.
- Research Insights: In cloud safety approaches, novel perceptions might be incorporated for stabilizing among utility, protection, and effectiveness.
References
- Daemen, J., & Rijmen, V. (2002). The Design of Rijndael: AES—The Advanced Encryption Standard. Springer.
- Ferraiolo, D. F., & Kuhn, D. R. (1992). Role-Based Access Controls. 15th NIST-NCSC National Computer Security Conference.
- Gentry, C. (2009). Fully Homomorphic Encryption Using Ideal Lattices. STOC.
- Hu, V. C., Ferraiolo, D. F., & Kuhn, D. R. (2015). Attribute-Based Access Control. Computer, 48(2), 85-88.
- Kandukuri, B. R., Paturi, V. R., & Rakshit, A. (2009). Cloud Security Issues. IEEE International Conference on Services Computing.
- Paillier, P. (1999). Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. EUROCRYPT.
- Wang, C., Wang, Q., Ren, K., Cao, N., & Lou, W. (2010). Towards Secure and Dependable Storage Services in Cloud Computing. IEEE Transactions on Services Computing.
- Xu, X., Weber, I., & Staples, M. (2019). Architecture for Blockchain Applications. Springer.
- Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends. IEEE International Congress on Big Data.
What cloud computing topic can I research?
There are several topics emerging in the field of cloud computing, but some are determined as interesting and effective. We provide few intriguing and recent research topics in cloud computing that could contain major realistic and educational implication:
- Edge Computing and Cloud Integration
Explanation: To decrease delay, enhance resource usage, and improve effectiveness, aim to examine in what way edge computing could be combined with cloud computing.
Possible Areas:
- Resource management and arrangement in edge-cloud platforms.
- Actual-time data processing and analytics at the edge.
- Infrastructures for consistent edge-cloud combination.
- Safety and confidentiality limitations in edge computing.
- AI-Driven Cloud Resource Management
Explanation: In what way machine learning and artificial intelligence are able to forecast faults, enhance resource allocation, and improve the performance of cloud data centers has to be investigated.
Possible Areas:
- Predictive maintenance employing machine learning.
- Cost improvement by AI.
- AI methods for dynamic resource allotment.
- AI-based load balancing and auto-scaling.
- Serverless Computing Optimization
Explanation: It is approachable to concentrate on enhancing the scalability, effectiveness, and cost-efficiency of serverless infrastructures.
Possible Areas:
- Resource management in serverless platforms.
- Performance benchmarking of serverless environments.
- Decreasing cold start delay in serverless operations.
- Safety limitations in serverless computing.
- Blockchain in Cloud Computing
Explanation: In cloud computing platforms, explore in what way blockchain mechanisms can improve performance, protection, and clarity.
Possible Areas:
- Safe and clear data sharing utilizing blockchain.
- Combining blockchain with cloud storage for improved protection.
- Blockchain-related access control and identity management.
- Immutable logging and audit trails with blockchain.
- Energy-Efficient Cloud Computing
Explanation: In order to decrease the energy utilization of cloud data centers when sustaining consistency and effectiveness, aim to construct suitable techniques.
Possible Areas:
- Renewable energy combination in cloud data centers.
- Assessing the carbon footprints of cloud services.
- Energy-effective resource management methods.
- Green cloud infrastructures.
- Cloud Security and Privacy
Explanation: By means of progressive encryption methods, safe data management approaches, and access control technologies, focus on improving data confidentiality and protection.
Possible Areas:
- Attribute-Based Access Control (ABAC) frameworks.
- Intrusion detection and prevention systems employing AI.
- Homomorphic encryption for safe cloud computing.
- Confidentiality-preserving data analytics.
- Multi-Cloud and Hybrid Cloud Strategies
Explanation: Specifically, for handling data and sources among numerous cloud suppliers and hybrid cloud platforms in an effective manner, it is appreciable to examine beneficial policies.
Possible Areas:
- Data interoperability and portability among clouds.
- Cost enhancement in multi-cloud platforms.
- Multi-cloud resource allocation and management models.
- Safety and adherence in multi-cloud configurations.
- Big Data Analytics in the Cloud
Explanation: For processing and examining huge quantities of data in cloud platforms, focus on exploring effective and scalable techniques.
Possible Areas:
- Scalable storage approaches for big data.
- Data visualization and interpretation tools.
- Actual-time data processing models.
- Machine learning frameworks for big data analytics.
- Cloud-Native Application Development
Explanation: By employing containers, microservices, and DevOps strategies, create effective approaches and models for constructing and implementing cloud-native applications.
Possible Areas:
- Container orchestration employing Kubernetes.
- Resistance and fault tolerance in cloud-native applications.
- Microservices infrastructure design trends.
- Continuous integration and deployment (CI/CD) pipelines.
- IoT and Cloud Integration
Explanation: To improve the abilities of data processing, storage, and analytics, aim to investigate the combination of Internet of Things (IoT) devices with cloud environments.
Possible Areas:
- Actual-time IoT data analytics.
- Edge computing for IoT data processing.
- Scalable infrastructures for IoT-cloud combination.
- Safety and confidentiality in IoT-cloud frameworks.
Research Proposal on Cloud Computing Security
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