The Psychology of Privacy in the Digital Age

Privacy is a psychological topic suffering from historical neglect – a neglect that is increasingly consequential in an era of social media connectedness, mass surveillance and the permanence of our electronic footprint. Despite fundamental changes in the privacy landscape, social and personality psychology journals remains largely unrepresented in debates on the future of privacy. By contrast, in disciplines like computer science and media and communication studies, engaging directly with socio- technical developments, interest in privacy has grown considerably. In our review of this interdisciplinary literature we suggest four domains of interest to psychologists. These are: sensitivity to individual differences in privacy disposition; a claim that privacy is fundamentally based in social interactions; a claim that privacy is inherently contextual; and a suggestion that privacy is as much about psychological groups as it is about individuals. Moreover, we propose a framework to enable progression to more integrative models of the psychology of privacy in the digital age, and in particular suggest that a group and social relations based approach to privacy is needed

The value of privileged access

European Journal of Philosophy, EarlyView

Privacy Disclosures Detection in Natural-Language Text Through Linguistically-Motivated Artificial Neural Networks

An increasing number of people are sharing information through text messages, emails, and social media without proper privacy checks. In many situations, this could lead to serious privacy threats. This paper presents a methodology for providing extra safety precautions without being intrusive to users. We have developed and evaluated a model to help users take control of their shared information by automatically identifying text (i.e., a sentence or a transcribed utterance) that might contain personal or private disclosures. We apply off-the-shelf natural language processing tools to derive linguistic features such as part-of-speech, syntactic dependencies, and entity relations. From these features, we model and train a multichannel convolutional neural network as a classifier to identify short texts that have personal, private disclosures. We show how our model can notify users if a piece of text discloses personal or private information, and evaluate our approach in a binary classification task with 93% accuracy on our own labeled dataset, and 86% on a dataset of ground truth. Unlike document classification tasks in the area of natural language processing, our framework is developed keeping the sentence level context into consideration