An Adaptive Privacy Policy Prediction for Classifying the Images as Public and Private for Secured Transaction - Kavitha, S; Girisha, H (International Journal of Computer Sciences and Engineering, 2025)
With the expanding volume of pictures clients offer through social locales, keeping up security has turned into a noteworthy issue, as exhibited by a late influx of advanced episodes where clients unintentionally shared individual data. In light of these episodes, the need of apparatuses to offer clients some assistance with controlling access to their common substance is evident. Toward tending to this need, we propose an Adaptive Privacy Policy Prediction (A3P) framework to offer clients some assistance with composing protection settings for their pictures. We look at the part of social connection, picture substance, and metadata as could be expected under the circumstances pointers of clients' security inclinations. We propose a two-level system which as indicated by the client's accessible history on the site, decides the best accessible security approach for the client's pictures being transferred. Our solution relies on an image classification framework for image categories which may be associated with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the generated policies will follow the evolution of users’ privacy attitude. We provide the results of our extensive evaluation over 5,000 policies, which demonstrate the effectiveness of our system, with prediction accuracies over 90 percent.