How Machine Learning Helps in Privacy and Protection

Ritu Kaushik — 2026-05-30

In today’s world, privacy and protection-related issues are becoming increasingly relevant day by day. It becomes a critical concern due to the rapid growth in the usage of social media platforms, internet usage, and smart usage of technology, along with its advancements. Individuals continuously share their personal data or information through online services. Individuals connect with one another with online networking platforms, due to which there is always a threat of data breaches, identity theft, unauthorized surveillance and phishing, etc. With the increasing use of Artificial Intelligence and Big Data Analytics, these issues are becoming more intense day by day, enabling the collection of large-scale datasets. This paper examines the causes of the increase of major privacy concerns like lack of awareness, excess data tracking and weak security mechanisms in digital systems. The study explores how machine learning techniques can be used to enhance data protection and ensure secure data processing. The paper concludes that achieving a balance between technological innovation and privacy protection is essential for building a secure and trustworthy digital environment in the future.

A Novel AI IoT-Based Approach for Autonomous Waste Segregation and Management in Sustainable Smart Urban Environments

K. Murugan — 2026-06-06

Rapid urbanization has resulted in a considerable rise in municipal solid waste, creating significant environmental and public health issues in contemporary cities. Conventional waste management systems frequently prove to be inefficient, labor-intensive, and deficient in real-time monitoring capabilities, leading to improper waste segregation and resource wastage. To overcome these challenges, this paper introduces an innovative AI-IoT (Artificial Intelligence of Things) strategy for autonomous waste segregation and management in sustainable smart urban settings. The suggested system combines Internet of Things (IoT) sensors with Artificial Intelligence (AI) algorithms to facilitate real-time detection, classification, and sorting of waste into categories such as biodegradable, recyclable, and hazardous materials. Sophisticated machine learning models, including computer vision techniques, are utilized to accurately identify different types of waste, while embedded sensors keep track of bin levels, environmental conditions, and collection schedules. Furthermore, the system integrates cloud-based data analytics to optimize waste collection routes, lower operational costs, and improve decision-making for urban authorities. Additionally, the framework fosters sustainability by enhancing recycling efficiency, decreasing reliance on landfills, and minimizing environmental pollution. The autonomous characteristics of the system lessen the need for human intervention, ensuring safer and more hygienic waste handling processes. Experimental analyses and simulations indicate that the proposed AIoT-based solution markedly enhances segregation accuracy, collection efficiency, and overall waste management performance when compared to traditional methods. This research aids in the advancement of intelligent, scalable, and eco-friendly waste management systems, aligning with the vision of sustainable smart cities.

Journal of IoT Security and Smart Technologies (e-ISSN: 2583-6226)

How Machine Learning Helps in Privacy and Protection

A Novel AI IoT-Based Approach for Autonomous Waste Segregation and Management in Sustainable Smart Urban Environments