# The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation
## 3 The Privacy Flag Observatory Architecture  
The overall Privacy Flag architecture is presented in Figure 1. The black framed subsystem is the Privacy Flag Observatory and its components, on which we focus on this paper. Its main objective is to inform users, developers, stakeholders and researchers on the level of adoption of good privacy and security practices, but also to issue warnings when insecure, obsolete or deprecated technologies are used. The Privacy Flag platform allows people to monitor and control their privacy with a user-oriented approach supported by a friendly interface and interactive menus. Furthermore, interested stakeholders can observe the level and rate of compliance with privacy-preserving practices and technologies for the most popular websites. Focusing on the framed part of the architecture in Figure 1, the Privacy Flag Observatory includes, as it is shown at the top left-hand corner of the figure, a _smartphone application_ and a browser _web add-on_, both operating on the users' devices. These tools are implemented as _Distributed Agents (DAs)_ that run both on the user's side and the server. Two types of DAs exist. One is the Privacy Flag Web add-on which is installed on the user's browser. Each time a user visits a new website that has not been previously evaluated, a set of automated security and privacy checks is initiated. A part of these checks is performed  
Figure 1: Overall platform architecture. by the Privacy Flag Web add-on locally, on the user's device, while the rest of the checks are executed remotely by and on the Privacy Flag Server. The reason for this decision is technical, based on the nature of each specific test. Some tests can be executed, more conveniently, on the Privacy Flag server, while others are easier to execute on the user's device. From an architectural perspective, we tried to offload, as much as possible, the computational effort of the Privacy Flag Web add-on to the Privacy Flag Server in order to achieve minimal impact on the users' devices. Therefore, the Privacy Flag Server is extensively used to perform the site evaluations. The completion of this evaluation step is user-transparent and does not require human intervention. A visual notification indicates whether evaluation feature is turned on so that users can deactivate or reactive it at any time. The evaluation results are _anonymously_ transmitted and stored in the Privacy Flag Database to be included in the analysis and extraction of the general privacy and security trends. The users can also choose to manually assess the website by answering ten short questions related to the information they received regarding the privacy policy and personal data management. Similarly, a Privacy Flag smartphone application or Privacy Flag SmartApp has been developed to assess mobile applications. The Privacy Flag SmartApp evaluates the installed applications on the mobile device, based on the number and the level of permissions that each application requires to operate. Finally, after the collective assessment and the corresponding analysis on the Privacy Flag Server, a color-based code scheme indicates the privacy status of each website or mobile application. It is important to clarify that the Privacy Flag SmartApp cannot detect malicious applications. Instead, it highlights the applications that require access to different sensitive subsystems of the mobile device. Therefore, these applications constitute a significant privacy risk and should be treated with caution. The exact nature of all the checks will be discussed in detail later in this paper. However, it is important to emphasize that due to space limitations it is not possible to extensively analyze all the subsystems of the Privacy Flag platform. Therefore, this work is focused on the Privacy Flag Observatory alone. A central component of the Privacy Flag Observatory architecture, as shown in Figure 1, is the _P_ivvacy Flag Database which contains submitted and deduced assessments provided by users and the distributed agents.