University of Twente Student Theses
Exploring Probabilistic Data Structures for Privacy-Preserving Pedestrian Dynamic Analysis
Bizhev, Hristo (2024) Exploring Probabilistic Data Structures for Privacy-Preserving Pedestrian Dynamic Analysis.
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| Abstract: | Monitoring pedestrian dynamics is critical for urban planning, resource allocation, and public safety. Traditional methods of data collection pose significant privacy concerns, necessitating the use of privacy-preserving techniques. This paper analyzes the functionality of five prominent probabilistic data structures for pedestrian counting and crowd monitoring and evaluates the performance of two selected ones, Bloom Filters and HyperLogLog. We explore their effectiveness in estimating set cardinality, union, and intersection sizes across varying parameters. Experimental results indicate that both data structures have similar accuracy, with relative errors around 0.43% for set cardinality and union estimations. However, Bloom Filters demonstrate significantly better performance in terms of execution time and memory usage, being five times faster and more than 40 times more space-efficient than HyperLogLog. Despite HyperLogLog's slightly better accuracy in intersection estimations, Bloom Filters' overall efficiency makes them more suitable for real-time applications. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/100950 |
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