RETRACTED ARTICLE: DQSCTC: design of an efficient deep dyna-Q network for spinal cord tumour classification to identify cervical diseases

Data privacy has grown to be of utmost importance in today's digitally driven world. Protecting sensitive information has never been more important due to the explosion of data across many areas. This abstract explores cutting-edge machine learning techniques for improving data privacy in the digital age.Artificial intelligence's subset of machine learning presents a viable way to overcome issues with data privacy. This study investigates how machine learning algorithms can be used to strengthen confidentiality protections in a range of applications. Machine learning models may uncover vulnerabilities and potential breaches in real time by analysing large information, offering proactive defence against cyber threats.We explore a number of data privacy topics, such as access control, encryption, and data anonymization, while emphasising how machine learning approaches might improve these procedures. We also cover how federated learning protects privacy during collaborative data analysis, enabling different parties to gain knowledge without jeopardising the integrity of the data.The importance of ethics and compliance in the creation and application of machine learning solutions for data confidentiality is also emphasised in this abstract. It highlights the necessity for ethical AI practises and highlights the difficulties in finding a balance between the preservation of privacy and the usefulness of data.This study investigates how machine learning could strengthen data confidentiality, paving the path for a more safe and considerate digital future. It highlights the value of interdisciplinary cooperation between data scientists, ethicists, and policymakers to fully utilise machine learning's promise in protecting our sensitive information in the digital world.

Data Privacy in the Digital Era: Machine Learning Solutions for Confidentiality

Dari,

Dhabliya,

Govindaraju

et al. 2024

Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity

Kalnawat,

Dhabliya,

Vydehi

et al. 2024

It has become essential to protect vital infrastructures from cyber threats in an age where technology permeates every aspect of our lives. This article examines how machine learning and cybersecurity interact, providing a thorough overview of how this dynamic synergy might strengthen the defence of critical systems and services. The hazards to public safety and national security from cyberattacks on vital infrastructures including electricity grids, transportation networks, and healthcare systems are significant. Traditional security methods have failed to keep up with the increasingly sophisticated cyber threats. Machine learning offers a game-changing answer because of its ability to analyse big datasets and spot anomalies in real time. The goal of this study is to strengthen the defences of key infrastructures by applying machine learning algorithms, such as CNN, LSTM, and deep reinforcement learning for anomaly algorithm. These algorithms can anticipate weaknesses and reduce possible breaches by using historical data and continuously adapting to new threats. The research also looks at issues with data privacy, algorithm transparency, and adversarial threats that arise when applying machine learning to cybersecurity. For machine learning technologies to be deployed successfully, these obstacles must be removed. Protecting vital infrastructures is essential as we approach a day where connectivity is pervasive. This study provides a road map for utilising machine learning to safeguard the foundation of our contemporary society and make sure that our vital infrastructures are robust in the face of changing cyberthreats. The secret to a safer and more secure future is the marriage of cutting-edge technology with cybersecurity knowledge.

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

In today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for techniques to create resilient machine learning (ML) systems that can withstand changing threats is discussed in this study.Data protection is an important component of securing ML environments. Every part of the process, from data preprocessing through model deployment, needs to be secured. In order to reduce potential vulnerabilities, this incorporates code review procedures, safe DevOps practises, and container security.System resilience is vitally dependent on on-going monitoring and anomaly detection. Organisations can respond quickly to security problems by detecting deviations from normal behaviour early on and adjusting their defences as necessary.A strong incident response plan is essential. To protecting machine learning ecosystems necessitates a comprehensive strategy that includes monitoring, incident response, model security, pipeline security, and data protection. By implementing these tactics, businesses may create robust machine learning (ML) systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes.