Aus SDQ-Institutsseminar
Termin (Alle Termine)
Datum Freitag, 13. Dezember 2019
Uhrzeit 11:30 – 12:35 Uhr (Dauer: 65 min)
Ort Raum 348 (Gebäude 50.34)
Vorheriger Termin Fr 6. Dezember 2019
Nächster Termin Fr 20. Dezember 2019

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Vortragende(r) Daniel Betsche
Titel Feature-based Time Series Generation
Vortragstyp Proposal
Betreuer(in) Adrian Englhardt
Kurzfassung Due to privacy concerns and possible high collection costs of real time series data, access to high quality datasets is difficult to achieve for machine learning practitioners. The generation of synthetic time series data enables the study of model robustness against edge cases and special conditions not found in the original data. A requirement to achieve such results in applications when relying on synthetic data is the availability of fine-grained control over the generation to be able to meet the specific needs of the user. Classical approaches relying on autoregressive Models e.g. ARIMA only provide a basic control over composites like trend, cycles, season and error. A promising current approach is to train LSTM Autoencoders or GANs on a sample dataset and learn an unsupervised set of features which in turn can be used and manipulated to generate new data. The application of this approach is limited, due to the not human interpretable features and therefore limited control. We propose various methods to combine handcrafted and unsupervised features to provide the user with enhanced influence of various aspects of the time series data. To evaluate the performance of our work we collected a range of various metrics which were proposed to work well on synthetic data. We will compare these metrics and apply them to different datasets to showcase if we can achieve comparable or improved results.
Vortragende(r) Viktoriia Trukhan
Titel Towards Differential Privacy for Correlated Time Series
Vortragstyp Masterarbeit
Betreuer(in) Christine Tex
Kurzfassung Differential privacy is the current standard framework in privacy-preserving data analysis. However, it presumes that data values are not correlated. Specifically, adversaries that are aware of data correlations can use this information to infer user’s sensitive information from differential private statistics. However, data correlations are frequent. In particular, values of time series like energy consumption measurements are frequently highly temporally correlated. In this thesis, we first introduce and critically review the notation of dependent differential privacy (DDP) introduced by Liu at. al (2016), which is a differential-privacy like privacy definition for spatially correlated data. Second, we adapt this notation and the respective privacy mechanisms to temporally correlated data. We evaluate our adaption on a real-world energy consumption time series showing that our mechanism outperforms the baseline approach. We conclude this work by stating in which direction the improvements of the mechanism might be done.
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