The rise of online learning has been accelerated by several factors, including technological advancements, the need for lifelong education, and global events such as the COVID-19 pandemic. As a result, numerous universities have significantly expanded their online education offerings. This shift has created a growing demand for effective online examination methods, particularly for software engineering courses that require coding tests. The widespread adoption of online exams has increased the need for online exam proctoring. However, this transition has raised significant concerns regarding potential privacy violations and intrusive surveillance practices. Despite the existence of frameworks attempting to address these issues, there remains a pressing need for a system that effectively preserves student privacy without compromising the integrity and fairness of online assessments. We propose PRIVITOR, a comprehensive proctoring framework addressing these issues. Our contributions include: (1) a novel approach to collect data for training proctoringspecific machine learning models, (2) an efficient anomaly detection classifier with an associated cheating detection algorithm, and (3) an innovative facial masking technique for privacy-preserving proctor-student interaction. Results show that our anomaly detection classifier achieves high accuracy while processing videos approximately ten times faster than existing eye-tracking algorithms. The facial masking technique effectively balances privacy protection and invigilation capabilities.