Access logs may offer service providers a lot of information about specific users. Depending on the type of the service offers, the operator is capable of obtaining the user’s IP, location, communication habits, device information and so on. In this paper, we analyze a sample instant messenger service that is operating for a certain period of time. In our sandbox, we gathered enough data to correlate user communication habits with their localization, and even contacts. We show how seriously metadata may impact the user’s privacy and make some recommendations about mitigating the quantity of data collected in connection with this type of services.

Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions' images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.