# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 3 Related Work  
When looking at the state of the art in terms of technical measures to minimize the information content of data, three fundamentally different approaches can be identified. First, base data can be withheld in whole [35] or in part [36]. Second, base data can be aggregated [37], or certain aspects of the data can be filtered out [38]. Third, the data of a particular instance can be _hidden_ in the bulk data [39]. Withhold Data.One of the most effective ways to protect sensitive information is to avoid sharing them with third parties and not let such data be processed at all. In practice, for instance, this would mean that access to certain sensors on an IoT device--and therefore their data as well--would be completely blocked [40]. As a result, the information captured by the sensor in question is no longer available for analysis. This, however, has a significant impact on data quality and the knowledge that can be derived from them. To mitigate this to some degree, the withholding of data can be bound to certain constraints. For instance, certain time slots or locations can be defined where the disclosure of the collected data are acceptable for the data subject [41]. Likewise, only those data items that contain particularly compromising information can be withheld, e.g., if the captured values are above or below a defined threshold [42]. From a technical point of view, this approach corresponds to the application of a _selection operator_\(\sigma\) adopted from relational algebra. That is, all tuples that do not satisfy a specified predicate are removed from the corresponding relation, i.e., a selection represents a _horizontal withholding of data_[43]. Instead of withholding the entire data from a sensor or a full dataset, only certain features of a sensor can be concealed in a fine-grained manner as well [44]. If a sensor captures multiple facts and combines them into a single measurement--e.g., a GPS sensor captures, among other things, longitude, latitude, and altitude--it is possible that only some of these features contain confidential information. In order to still be able to process the measurements of this sensor, only the affected feature can be withheld in this case. From a technical point of view, this corresponds to the application of a _projection operator_\(\pi\) adopted from relational algebra. That is, a subset of the available attributes is removed from all relations, i.e., a projection represents a _vertical withholding of data_[45]. Filter Data.However, withholding data represents a sledgehammer approach that severely impairs data quality and quantity. Filtering techniques take a more sophisticated approach. Here, only sensitive information is filtered out of the data. To this end, there are two opposing approaches: either the data are _condensed_, or they are _enriched_ with synthetic data. When condensing data, e.g., the data can be grouped by certain attribute and then only a single synthetically generated dataset per group, which is representative of the respective group, is forwarded for processing. From a technical point of view, this corresponds to the application of an _aggregate operator_\(\mathcal{G}\) adopted from relational algebra. That is, all relations with common attribute value are initially grouped and then an aggregate function, e.g., sum or median, is applied to each group [46]. Instead of grouping all records by their attribute values, all records collected within a given time frame can be aggregated, alternatively [47]. However, in doing so, the data quantity is severely reduced since \(n\) records are mapped to a single aggregated value. To address this, data interpolation initially reduces the amount of data--and thus the disclosure of sensitive information--by processing only every \(n\)-th record of a data source. The omitted data points are then filled with suitable _mock data_ to maintain the original amount of data [48].