# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### DNA Sequence Classification  
The analysis of human DNA samples has many useful applications. While in the early days, DNA analysis was mainly used in forensics to uniquely identify individuals--via the so-called _genetic fingerprint_--other areas of application have emerged over time as analytical  
Figure 6: Two Fundamentally Different Modes of Operation of Recommender Systems. methods have become more advanced. For instance, it can be used to establish paternity in custody and child support litigation or to trace ancestry over hundreds of thousands of years to study population genetics. Furthermore, DNA analysis can also be used in the medical domain to diagnose inherited disorders and human diseases [101]. Epigenetic modifications, such as DNA methylation, can be used to detect common diseases. For instance, aberrant DNA methylation of imprinted loci can often be observed in connection with certain types of cancer. In addition, autoimmune diseases, metabolic disorders, and neurological disorders are closely related to DNA methylation. Medicine could therefore make significant progress if a deeper understanding of epigenetic mechanisms leading to these diseases is available [102]. Although insights into the mechanisms of DNA demethylation have already been gained, there are still many correlations that have been observed but not yet systematically studied. To this end, however, extensive analyses are required to comprehend all relevant factors [103]. Instead of striving to explain the complex correlations, a reliable classification is already sufficient for a medical diagnosis. For instance, normal and malignant tissue samples from human liver and lung can be studied in order to be able to classify future tissue samples into \"normal\" and \"malignant\" based on these findings [104]. Instead of an analysis by a medical expert, artificial intelligence techniques can be used for DNA sequence classification. For this purpose, a large number of labeled samples are collected and used to train a model. This model recognizes the relevant genome sequences that are indicative of a particular disease [105]. Figure 7 is a simplified illustration of how this can be completed. First, suitable target data are selected. Preprocessing steps are applied to these data to structure them and prune errors or missing values. Then, the features relevant for the analysis are extracted. In the given example, sections of the DNA helix of the samples, i.e., sequences of base pairs. With these data, a _convolutional neural network_ (_CNN_) is trained. In a CNN, there are multiple hidden layers in addition to an input and output layer. During the training phase, weights are allotted to describe how much certain nodes from one layer contribute to the inputs of the subsequent layer (and thus, eventually, to the final outcome). This allows one to classify new unlabeled data. The advantage of a CNN is that pooling allows one to discard redundant information early on, whereby even large amounts of data can be processed efficiently. This makes it also more robust against overfitting, i.e., it is possible to detect variants of a disease. Furthermore, a CNN can subsequently adjust itself to respond to shifts in circumstances [106]. However, the training of such a CNN requires the establishment of large repositories of annotated genomic data. Although this may represent a key element for future discoveries in human disease, it inevitably also raises a variety of privacy concerns. Even if the metadata of the samples are fully anonymized, the DNA itself represents a unique fingerprint. All knowledge gained from a sample--e.g., ancestry, parenthood, pre-existing conditions, and genetic disorders--can therefore be uniquely traced back to a data subject [107]. Another inherent issue with CNN is its black-box approach. The reason for a certain decision cannot be explained even by domain experts [108]. The layers of a CNN consist of hundreds  
Figure 7: Application of a Convolutional Neural Network for the Classification of DNA Samples.