# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 1 Introduction  
In 1991, [PERSON] envisioned the computer for the 21 st century [1] as a pervasive system that ubiquitously surrounds us, constantly adapting to its context and our current needs. Although this vision did not fully materialize, the _Internet of Things (IoT)_ is a major step in this direction. Here, various sensors are integrated into everyday objects, enabling them to monitor their surroundings and react to it. Furthermore, all of these IoT-enabled devices, often referred to as _smart devices_, are interconnected and can communicate with each other. Thus, smart devices have a virtually unlimited data stock at their disposal [2]. The full potential of the gathered data can only be exploited if they are interlinked and comprehensively analyzed [3]. Yet, these data, which are labeled _big data_, are generated at high _velocity_ and in high _volume_. Profound data processing is therefore not possible on the mostly lightweight smart devices, as they do not have the necessary resources and computing power to do this at an adequate speed and scale. Moreover, there is a high _variety_ in the data in terms of schemata and data formats. Thus, extensive data preparation is required to merge these data, which also exceeds the capacities of smart devices [4]. The processing of the captured raw data therefore usually takes place in a powerful backend system. There, the raw data are initially refined. That is, they are cleansed--i.e., missing or erroneous data are treated--and transformed--i.e., different schemata andformats are harmonized [5]. Subsequently, the refined data can be interlinked, further preprocessed, and analyzed [6]. In analogy to the concepts of the knowledge management, three stages can be differentiated. _Data_ refers to the unprocessed and unfiltered raw data collected by the smart devices. The refined and interlinked data are termed _information_. _Knowledge_ is generated only when the information is analyzed, and the findings are interpreted [7]. Such knowledge provides the foundation for so-called _smart services_. A smart service is any kind of data-driven digital service that is able to adapt to the data and thus offering users the greatest possible utility in their current situation. Smart services can be very small scale, e.g., an adaptive application on a smartphone, as well as highly complex, e.g., when several smart devices interact with each other via actuators [8]. Examples of smart services can be found in the public, industrial, and private sectors, e.g., in the context of _smart cities_[9], _Industry 4.0_[10], and _eHealth_[11]. While such smart services are very appealing since they significantly facilitate the lives of their users in a wide variety of situations, they also pose a serious threat. As we are constantly surrounded by smart devices, they are able to quantify all aspects of their users' lives as well as the lives of innocent bystanders. The thereby gained knowledge provides deep insights into the privacy of these individuals [12]. Data protection laws, such as the _European General Data Protection Regulation_ (_GDPR_) [13], therefore entail principles, such as _data minimization_ (Article 5(1)(c)), and mandate _data protection by design_ (Article 25). However, this leads to what is known as the _privacy paradox_--although users crave the best possible protection of their privacy, they still do not want to refrain from using smart services, which in turn requires them to share their private data with these services [14]. Therefore, this paradox must be resolved by concealing sensitive knowledge patterns contained in the data without significantly impairing the general data quality. Privacy measures that are too restrictive would render the data unusable, while measures that are too shallow would jeopardize privacy [15]. In order for this balancing act to succeed, however, privacy measures must be tailored to the data and how they are processed. Therefore, we investigate whether state-of-the-art privacy mechanisms for smart services are up to this task.