Active visual attention (AVA) is the cognitive ability that helps to focus on important visual information while responding to a stimulus and is important for human-behavior and psychophysiological research. Existing eye-trackers/camera-based methods are either expensive or impose privacy issues as face videos are recorded for analysis. Proposed approach using blink-rate variability (BRV), is inexpensive, easy to implement, efficient and handles privacy issues, making it amenable to real-time applications. Our solution uses laptop camera/webcams and a single blink feature, namely BRV. First, we estimated participant's head pose to check camera alignment and detect if he is looking at the screen. Next, subject-specific threshold is computed using eye aspect ratio (EAR) to detect blinks from which BRV signal is constructed. Only EAR values are saved, and participant's face video is NOT saved or transmitted. Finally, a novel AVA score is computed. Results shows that the proposed score is robust across participants, ambient light conditions and occlusions like spectacles.