# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 1 Introduction  
Since in 2011 the World Economic Forum coined the phrase \"data will be the new oil\" [1], this allegory of data as the most valuable asset to be exploited has become increasingly entrenched [2]. Data as a resource are highly relevant in any domain, including research [3], economy [4], the public sector [5], and even private life [6]. None of the so-called smart services can operate without processing vast amounts of data--the better the quality and quantity of the data, the better the outcome [7]. This fact is often seen very positively, since data, unlike oil, are an almost indefinite resource. Today, it is possible for every user to be a data producer, and due to the _Internet of Things (IoT)_, data can be gathered in any domain [8]. Moreover, unlike oil, processing data are clean, and, since data are not consumed when they are processed, they represent the ultimate renewable resource [9]. However, this should not be perceived quite so optimistic. Just like oil, data are virtually worthless in their raw state. Thorough processing techniques are required in order to refine them, and, in this process, enriching their information contentand generating a value [10]. Elaborate data science strategies are therefore needed to derive essential knowledge from raw data [11]. This is often illustrated by means of the _Data Pyramid_[12] (also known as _DIK Hierarchy_--data, information, knowledge). This three tier pyramid is shown in Figure 1. It is made evident that data are not only the foundation of this pyramid (and thus the source of all knowledge), but that they are also available in large numbers. This is due to the fact that, nowadays, almost every device is equipped with a variety of sensors and has the connectivity needed to share the captured data with others [13]. These data, however, are raw, i.e., they are unstructured and also contain some errors. Only by means of pre-processing steps (e.g., data cleansing or linking previously incoherent data), raw data become useful and valuable information. The actual analyses can only be carried out on the basis of this information. By applying highly use-case-specific data processing techniques, knowledge can be extracted from the information, which can then be used, for instance, by a smart service. Although the quantity was reduced in the process, the aggregation of the raw data has led to an increase in significance and thus value [14]. This data refinement process therefore involves data processing at two stages: First, raw data must be prepared using data wrangling techniques to extract processable assets [15]. Then, processing techniques from the field of data mining or machine learning can be applied to generate an added value for the intended use case [16]. Although it may seem at first that the latter is the more valuable task, studies confirm that the foundation of any successful data analysis is actual its preceding data preparation [17]. This, however, raises security concerns when dealing with sensitive data [18]. In the course of data refinement, the data processor gains deep insights into the data. When pre-processing and analyzing personal data [19] or confidential data [20], it is therefore essential for the data subjects and data owners to ensure that no private information is disclosed to third parties involved in the process [21]. However, this confronts data owners with the _privacy paradox_: The best way to protect non-disclosure interests of data owners and data subjects is to provide data processors with no data at all, or at least only highly distorted data. This, however, also implies that no knowledge can be gained from the data, and smart services that rely on it would be either inoperable or ineffective. A user only experiences the best possible level of service if a data processor is able to derive the highest possible degree of knowledge from all available data. Unfortunately, this also means that a data owner has to expose all data and thus lose control over his or her data entirely.