# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### Security for UAVs over IoT
#### 2.4.2 Privacy Component  
Privacy protection in the IoT domain is divided into two main directions: (a) protection of collecting, acquiring, and distributing sensitive information such as faces, body silhouettes, objects, and license plates, by \"flying\" things such as drones; and (b) protection of observing and eliciting patterns, such as the number, duration, and diversity of connections, all of which can be used as signatures of IoT devices. The former direction is also referred as \"user privacy\", while the latter one is known as \"thing privacy\".  
Privacy protection is a challenging goal in many research fields where measuring devices take place. For example, in Reference [52] a wide range of sensors measure various types of information; however, the concept fully protects the privacy of all registered users. To ensure anonymity, the group signature and data of all devices are communicated via TLS protocol with an authentication mechanism. The integrated group signature method appears to be efficient to protect the identity of the measuring devices. A group signature is used to encrypt all data in order to avoid the reception of dummy data by the reporting service. Similarly, in Reference [53], the proposed work provides identity confidentiality of both parties (the end user who requests data and the owner of the mobile device). In that manner, the end user cannot associate the mobile device and measured data.  
Current solutions for \"user privacy\" in \"flying\" things are focused on filters aiming to remove sensitive information. Most of these systems are based on computer vision analyzing video content [54]. In the case of mobile things, such as drones, since they can get close to targets and capture the same scene from different points of view, they are able to gather sensitive personal data, adding a new dimension to issues related with privacy and protection solutions. Several state-of-the-art privacy filters were introduced, aiming also to keep a balance between privacy issues and surveillance effectiveness. Privacy filters include simple filters such as pixelization, masking blurring, as well as more advanced morphing [55] and reversible warping [56] filters. Recently, several researchers [57; 58; 59; 60] proposed new features and approaches for drone-based surveillance that affect visual privacy. Regarding the acquired audiovisual data, a number of approaches have been proposed that employ encryption techniques [61; 62; 63]. However, such techniques cause the corruption of large parts of the original image, making the intelligibility task practically impossible. Other solutions use reversible scrambling applied in the compression-specific domain of a particular video format [64; 65; 66], improving the visual result, but heavily depending on the employed compression algorithm.  
\"Thing-privacy\" breaches arise because it is still possible to observe patterns such as the number, duration, and diversity of connections, all of which can be used as the signatures of IoT devices. Existing schemes that preserve privacy in identity management are mostly centralized solutions [67]. Data provenance can provide data trackability, not just for data privacy but also for data-quality assurance and data-management transparency [68; 69].