# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Image Privacy  
As described in Section 2.4, image analysis can be used to identify people in photos. This raises a lot of privacy concerns. In order to make privacy-sensitive parts of an image obliterated, it seems to be a straightforward approach to blur these parts extensively. However, this has a negative effect on the quality of the image (and thus on its processability), especially if the blurring affects large parts of the image. Yet, if only small areas are blurred, this obfuscation can be undone in the data refinement process [146]. Even a full redaction of individual objects in the image does not reliably protect privacy. For instance, faces (i.e., exactly the kind of objects that are particularly privacy-sensitive) can still be recognized by means of artificial intelligence [147]. Special obfuscation techniques are therefore needed to conceal the privacy-sensitive areas of an image in a manner that is robust against reconstitution. To this end, _Singular Value Decomposition_ is used to create a mask for the areas in question. This mask not only blurs the area but also adds features of other pictures to that area. This way, the result has similarities to several other images. Therefore, the blurred object can no longer be uniquely identified [148].  
However, there are two problems from a data subject point of view. First, the manual selection of objects on an image that need to be protected is very cumbersome and time consuming. Second, guidance is needed in selecting the appropriate obfuscation method. For instance, if images shall be shared on social media, the obfuscation techniques must have as little impact on the visual quality as possible. Figure 12 shows what a computer-aided method for solving these two problems looks like.  
Initially, the original image is analyzed using deep learning. In this process, the deep features are extracted, i.e., the components that are significant for the recognition of the depicted objects in the underlying model. Based on these deep features, the image is then partitioned into a set of semantic object regions. These regions describe connected areas on the image, e.g., a person, an animal, or an object, while background objects are ignored. Using a classification, all of these regions are then assigned to a semantic meaning, e.g., bystander or child. For these classes, a privacy sensitivity is determined, which indicates how much the respective object needs to be protected [149]. In our example, bystanders receive the protection level orange, while children receive the protection level red. For persons, the semantic region can also be restricted to the face since this usually has the highest privacy sensitivity. To this end, face detection can be used to determine the region to be masked. Depending on the level of protection required, different techniques can be used that affect the visual quality of the image to a greater or lesser extent, e.g., blanking, scrambling, or blurring [150]. This way, a privacy-friendly version of the image suitable for social media can be generated.  
The key strengths and weaknesses of image privacy approaches are the following:  
Strengths:  
Sensitive content can be concealed specifically and according to individual privacy requirements.  
The data quality of the main components of an image is fully preserved by the image privacy approaches.  
Weaknesses:  
Privacy is a highly personal experience. In image privacy approaches, however, the owner or provider of the image decides which privacy requirements apply to the persons visible in an image.  
Deep learning is used for the initial image analysis. This means, however, that the original, unaltered image is thoroughly analyzed, and knowledge is generated. This means that much more sensitive knowledge is generated than would otherwise be the case.  
Figure 12: A Process for Making Privacy-Friendly Versions of Portraits with Sensitive Content.