An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering

Ajani, Samir N.; Wanjari, Mangesh

doi:10.1109/cicn.2013.106

Cited by 31 publications

(28 citation statements)

References 4 publications

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“…To protecting machine learning ecosystems requires a holistic approach that includes data security, model security, pipeline security, ongoing observation, and incident response. By using these all-encompassing tactics, businesses can create resilient ML systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes [6]. This article explores each of these aspects in depth, offering organisations looking to defend their machine learning ecosystems against new threats useful insights and suggestions.…”

Section: Figure 1: Systematic View Of Proposed Modelmentioning

confidence: 99%

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

E3S Web Conf.

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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.

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Section: Figure 1: Systematic View Of Proposed Modelmentioning

confidence: 99%

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…An access control mechanism with a final lock combined with cloud computing is presented by Roy et al [6]. For the secure exchange of medical data, Edemacu and colleagues discuss the resistance to collaboration in access control [7].…”

Section: Review Of Literaturementioning

confidence: 99%

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

Singh,

Limkar

2023

IJRITCC

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The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture.

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“…A set of points called seeds or generators is specified beforehand. For each seed there will be a corresponding region consisting of all points closer to that generator than to any other [10]. Hence, a Voronoi Diagram for a given data set can be constructed by computing the perpendicular bisectors of the line segments between the pairs of data points.…”

Section: Voronoi Diagrammentioning

confidence: 99%

Parametric Design Techniques Apply to Creative Hollow out Product Design with 3d Voronoi Patterns

Chang¹

2021

JCC

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The creation of hollow out art includes a variety of materials, techniques and categories, its content mostly emphasizes the ancient philosophy performance of the alternation of virtual and real, and Yin-Yang depends on the essence of Chinese culture deduction. If this feature is applied to product design, in addition to emphasizing functional orientation, this traditional arts integration with the new media, will give users a different visual inspire. This thesis is mainly in view of the importance of hollow out art in Chinese cultural heritage, and the 3D hollow out production craft has gradually lost. Therefore, the Delaunay triangle is constructed based on the Convex Hull interpolation algorithm, and the Voronoi Diagram feature is constructed based on the Divide and Conquer algorithm. And with Rhino modeling software as the main body, combined with the application of the parametric plug-in design program (GH), the 3D models of the parametric creative hollow pen holder and the parametric creative hollow lampshade were respectively completed. The traditional craftsmanship is integrated into the modern manufacturing process with innovative techniques, and the Chinese cultural spirit and beauty of nature are successfully connected.

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An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering

Cited by 31 publications

References 4 publications

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

Parametric Design Techniques Apply to Creative Hollow out Product Design with 3d Voronoi Patterns

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