# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Privacy-Preserving Time-Series Data  
As outlined in Section 2.2, the processing of time-series data is always about identifying temporal patterns and forecasting trends. Privacy-preserving measures for these data must therefore not prevent such processing techniques. Rather, the aim is to conceal particular values or short sections of the time series that contain a particularly high level of sensitive information. This can be either rule-based (e.g., all values above or below a certain threshold) or time-based (e.g., all data recorded within a certain time window). There are basically two strategies to achieve this. On the one hand, the amount of data can be reduced in order to reveal less knowledge. On the other hand, the amount of data can be amplified by additional fake data in order to hide the actual values [127]. Figure 10 shows these two opposing strategies. To achieve data reduction, single values can be deleted from the series if only a few and widely distributed data points are concerned. The resulting gaps can be filled by _interpolation_ so that the progression is still coherent [128]. To close gaps in time series where interpolation reaches its limits, e.g., due to the complexity of the progression or as the gaps are too large, machine learning techniques can be used to logically fill the missing parts [129]. If details in general shall be removed from a time series, _data-smoothing_ approaches can be used. Originally, these approaches were primarily developed to compress data by removing less relevant parts. This removes noise at the same time. In the context of time-series data, the _discrete cosine transform_ is used for this purpose in particular. An input signal is transformed into a finite sum of weighted trigonometric functions with different frequencies, representing a close approximation of the original data. As a result, the time series is smoothened [130]. With regard to privacy, however, this has another advantage, as all details of the single measuring points are wiped, and only the temporal progression remains. By using a _continuous wavelet transform_, the time-series data can be compressed even further [131]. _Information emphasizing_ is thereby achieved, i.e., only a chronological sequence of high points and low points is recognizable. A contrary strategy to protect privacy is _adding noise_ to the time series. For instance, an artificial _Gaussian noise_ can be generated, which is used to blanket the time-series data. Figure 10: Strategies for Protecting Private Information in Time-Series Data. The parameters of the Gaussian random variable can be used to control how much impact the noise has on the time-series data. In this way, not only each discrete data point can be distorted but also, in the case of particularly strong noise, the progression of the time series itself [132]. While this very simple approach sounds promising at first, it has a significant drawback, as noise filtering is a de facto standard in the data refinement of time-series data. With the help of artificial neural networks, noise can be reliably eliminated on the fly [133]. One reason why such noise can be easily detected and even removed as an anomaly is that its frequency is completely different from that of the time series. Using a sequence of high-pass filters and low-pass filters, a time series can be decomposed into individual frequency bands. The bands affected by noise can be removed, and the remaining bands can be composed to the denoised time series [134]. However, there are also algorithms such as _SNIL_ (_spread noise to intermediate wavelet levels_), which distribute the noise over all frequency bands of a time series and are therefore robust against such denoising [135]. The key strengths and weaknesses of privacy approaches for time-series data are the following:  
Strengths:  
Privacy techniques can be used to conceal both individual data points as well as data histories in time-series data. The data quality of certain aspects (e.g., temporal trends or relevant data points) can be maintained.