Blockchain and Machine Learning Integration for Data Privacy and Security

2024-05-06T20:27:19+03:00

Purpose: In today's world, data is gathered without any particular objective in mind; every action taken by a computer, or a human being is documented, and the data is studied in the future, if it is considered important to do so. The data will be subjected to several steps for analysis by a variety of parties, which raises the issue of trust in this context. There is a risk that the organizations involved in the analytical stages may abuse the data, which might contain private or sensitive information. Consequently, data privacy considerations should be carefully considered at this time.

Methodology/Findings: A definition of "data privacy" is the practice of limiting access to information according to how important it is. People are usually very comfortable giving out their names to strangers, but they'll wait to give out their mobile phone numbers until they're more familiar with the individual. In this era of digital technology, important personal information is often the target of individuals' efforts to protect their data. From a business's point of view, data privacy encompasses more than just employees' and consumers' private information. Data privacy issues are often believed to be a barrier to the widespread adoption of AI and ML-driven technology. The reason for this is that ML can only be trained and tested on very large data sets.

Implications to Theory, Practice and Policy: Imagine a world where trust is impossible to establish; here is where Blockchain technology might be useful. Blockchain uses the data anonymously. In this study, we provide a solution that ensures data security by integrating Blockchain technology with machine learning (Alfandi et al., 2020).

Optimizing Resource Allocation in Cloud Computing Using Machine Learning

2024-05-06T21:27:23+03:00

Purpose: A key component of large-scale distributed computing is the allocation of resources, as computer networks cooperate to address complex optimization problems. To get the most out of computers in general, or throughput, is the goal of resource allocation in this case. When it comes to distributed computing, there are two main varieties: grid computing and cloud computing. In grid computing, many geographically dispersed clusters are linked and made available to the general public.

Materials and Methods: We looked at Monte Carlo Tree Search and Long-Short Term Memory and examined how efficient they were. By maintaining consistent traffic patterns, the simulation demonstrated that MCTS performance was improved. However, such a strategy is difficult to implement due to the potential speed with which traffic patterns may alter. A successful service level agreement (SLA) was achieved, and the issue was shown to be fixable using LSTM. We compare the suggested model to different load-balancing algorithms to find the one that best allocates resources.

Findings: The results show that compared to the state-of-the-art models, the suggested model achieves an accuracy rate that is 10-15% higher. The suggested model lowers the error percentage rate of the average request blocking likelihood of traffic load by around 9.5-10.2% when compared to the predictions of existing models. Therefore, the proposed method has the potential to enhance network utilization by reducing the amount of time required by memory and the central processing unit.

Implications to Theory, Practice and Policy: One advantage of the new method is a more robust forecasting strategy in comparison to earlier models. Using firefly algorithms, future research will construct a cloud data center that employs a variety of heuristics and machine learning methodologies to load balance the energy cloud (Oshawa et al., 2022).

European Journal of Technology

Blockchain and Machine Learning Integration for Data Privacy and Security

Optimizing Resource Allocation in Cloud Computing Using Machine Learning