data privacy

This study examined the ethical concerns surrounding targeted advertising and data privacy among students of the Department of Mass Communication, University of Benin. Anchored on the Social Contract, Utilitarianism, and Stakeholder theories, the study explored consumers’ awareness of how their data is collected and used, the ethical implications of targeted advertising, and the effectiveness of existing data privacy regulations. Using a survey design, data were collected through structured questionnaires distributed online, with 114 valid responses analyzed using simple percentages. Findings revealed that most respondents were aware that advertisers use their personal data to tailor advertisements, though many expressed discomfort over the lack of transparency and consent. While awareness of Nigeria’s Data Protection Regulation (NDPR) was moderate, respondents believed its enforcement was weak. The study concluded that targeted advertising, though beneficial for personalization, raises serious ethical issues relating to privacy, manipulation, and consent. It recommended stricter enforcement of data protection laws, increased public awareness of data rights, and adoption of transparent and ethical advertising practices.

Machine learning has become an important technology used in many areas such as healthcare, banking, education, and mobile applications. However, traditional machine learning methods usually require large amounts of data to be collected and stored in one central location, which can create privacy and security concerns. This project focuses on the design and implementation of a Federated Learning system for privacy-preserving machine learning. Federated Learning is a modern approach that allows multiple users or organizations to train a machine learning model together without sharing their raw data. Instead of sending personal or sensitive information to a central server, each participant trains the model on their local device and only shares model updates. This helps to protect privacy while still allowing the model to learn effectively. In this project, a federated learning prototype was developed using Python and machine learning tools. The system was designed with features such as secure aggregation and differential privacy to improve data protection and reduce the risk of information leakage. The performance of the system was tested using distributed datasets to simulate real-world situations where data is stored in different locations. The results showed that the proposed system was able to maintain a good level of model accuracy while protecting user privacy. It also demonstrated that federated learning can be a practical solution for organizations that need to use machine learning without exposing sensitive data. The study concludes that federated learning has great potential for institutions such as the University of Benin and other organizations in Nigeria that require secure and privacy-aware machine learning solutions.