# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 1 Introduction  
Personal mobile and IoT systems, including wearable sensors, smart objects, smartphones, and other devices that continuously acquire data, are a fundamental source of 'Big Data', and therefore an enabler of the data-based economy, as universally recognized. They are also a source of 'Small Data', defined as the portion of Big Data relating to an individual. Small Data are frequently shared by users in the form of digital content such as images or videos. The widespread nature of personal IoT ecosystems will therefore increase the amount of private data that is shared by orders of magnitude, as well as the entities from which the data can be acquired, aggregated and processed. This fact determines the relevant privacy issues that are of concern to people and authorities. For instance, the European legislation has recently enshrined the General Data Protection Regulation, which poses stringent rules on the protection of personal data and informed consent. In particular, it states that the consent request must be presented in an understandable and easily accessible form, using simple and clear language. At present, however, the privacy policies of mobile and IoT devices and apps, including those for online social networking, are specified in text documents that are difficult to understand for ordinary users. In fact, recent studies have shown that when it is explained to mobile app users, what data are actually available to service providers and what the privacy implications are, users often feel surprised and deceived [1]. At the time of writing, there are no integrated and user-friendly mechanisms that allow users to understand the privacy implications of sharing their data in mobile and IoT ecosystems and to specify their privacy policies accordingly. Current privacy protection solutions cannot be successfully applied to the mobile and IoT scenarios. Indeed, individuals cannot cope with the complexity of understanding eachservice or device's privacy preferences and the implications of its settings, which include what can be gleaned from analytics when merging information from multiple sources [2]. Numerous studies have proposed different tools and formalisms for expressing privacy preferences in various application contexts [3]. However, it is not realistic for users to manually specify their preferences for each possible use case. Furthermore, several studies have shown that users often set contradictory policies because they are influenced by contingent factors or by misunderstanding [4]. It is therefore necessary to identify artificial intelligence (AI) methods to support the user in understanding the privacy policies of services and app, and in suggesting the most appropriate choice regarding the sharing of private content. In this paper, we address this challenge by proposing AI-based techniques for assisting the user in taking informed privacy decisions about new app/devices and data sharing in social networks. We propose a technique relying on machine learning and natural language processing to automatically parse privacy policies written in natural language, recognize the privacy-relevant entities in the text, and answer the user's questions about the privacy practices of the provider regarding certain private data types. We also present advanced feature extraction methods and a recognition algorithm to analyze user's images in order to suggest the most appropriate sharing option. We experimented with our techniques on real-world datasets, and the results show the effectiveness of our methods. We also developed a preliminary prototype of an envisioned system for the integrated support of users' privacy choices, which includes a chatbot that can communicate in natural language with the user. The main contributions of our work are the following.