On the Utility of Privacy Measures for Battery-Based Load Hiding: Unterschied zwischen den Versionen

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|termin=Institutsseminar/2022-04-22
|termin=Institutsseminar/2022-04-22
|vortragsmodus=in Präsenz
|vortragsmodus=in Präsenz
|kurzfassung=Battery based load hiding gained a lot of popularity in recent years as an attempt to guarantee a certain degree of privacy for users in smart grids.  
|kurzfassung=Hybrid presentation : https://kit-lecture.zoom.us/j/67744231815
Our work evaluates a set of the most common privacy measures for BBLH. For this purpose we define logical natural requirements and score how well each privacy measure complies to each requirement. We achieve this by scoring the response for load profile altering (e.g. noise addition) using measures of displacement.  
Battery based load hiding gained a lot of popularity in recent years as an attempt to guarantee a certain degree of privacy for users in smart grids. Our work evaluates a set of the most common privacy measures for BBLH. For this purpose we define logical natural requirements and score how well each privacy measure complies to each requirement. We achieve this by scoring the response for load profile altering (e.g. noise addition) using measures of displacement. We also investigate the stability of privacy measures toward load profile length and number of bins using specific synthetic data experiments.
We also investigate the stability of privacy measures toward load profile length and number of bins using specific synthetic data experiments.
Results show that certain private measures fail badly to one or many requirements and therefore should be avoided.
Results show that certain private measures fail badly to one or many requirements and therefore should be avoided.
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Aktuelle Version vom 19. April 2022, 10:01 Uhr

Vortragende(r) Hatem Nouri
Vortragstyp Bachelorarbeit
Betreuer(in) Vadim Arzamasov
Termin Fr 22. April 2022
Vortragsmodus in Präsenz
Kurzfassung Hybrid presentation : https://kit-lecture.zoom.us/j/67744231815

Battery based load hiding gained a lot of popularity in recent years as an attempt to guarantee a certain degree of privacy for users in smart grids. Our work evaluates a set of the most common privacy measures for BBLH. For this purpose we define logical natural requirements and score how well each privacy measure complies to each requirement. We achieve this by scoring the response for load profile altering (e.g. noise addition) using measures of displacement. We also investigate the stability of privacy measures toward load profile length and number of bins using specific synthetic data experiments. Results show that certain private measures fail badly to one or many requirements and therefore should be avoided.