Szczegóły publikacji
Opis bibliograficzny
IEEE BigData Cup 2022 report : privacy-preserving matching of encrypted images / Marcin Szczuka, Andrzej Janusz, Bogusław CYGANEK, Jakub GRABEK, Łukasz Przebinda, Andżelika Zalewska, Andrzej Bukała, Dominik Ślęzak // W: 2022 IEEE international conference on Big Data [Dokument elektroniczny] : Dec 17 - Dec 20, 2022, Osaka, Japan : proceedings / ed. by Shusaku Tsumoto, [et al.]. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — Dod. ISBN: 978-1-6654-8046-8, 978-1-6654-8044-4. — e-ISBN: 978-1-6654-8045-1. — S. 6471-6480. — Bibliogr. s. 6479–6480, Abstr. — Publikacja dostępna online od: 2023-01-26. — B. Cyganek, J. Grabek - dod. afiliacja: MyLED, Kraków, Poland
Autorzy (2)
- Szczuka Marcin
- Janusz Andrzej
- AGHCyganek Bogusław
- AGHGrabek Jakub
- Przebinda Łukasz
- Zalewska Andżelika
- Bukała Andrzej
- Ślęzak Dominik
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 145671 |
|---|---|
| Data dodania do BaDAP | 2023-03-10 |
| Tekst źródłowy | URL |
| DOI | 10.1109/BigData55660.2022.10020599 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | 2022 IEEE international conference on Big Data |
Abstract
We summarize the results of IEEE BigData 2022 Cup: Privacy-preserving Matching of Encrypted Images - a data mining challenge organized at the KnowledgePit platform in association with the IEEE BigData 2022 conference. We describe the challenge in the hearth of the competition task, as well as the data acquisition and preparation steps. We also provide a brief overview of the top-performing solutions submitted by participants. Finally, we present results of the post-competition data analysis, in which we consider the similarity of solutions submitted by various teams in terms of their errors on the test data. We conclude this analysis by discussion of the significance and impact of the competition results on the underlying problem of constructing effective and efficient anonymization algorithms for monitoring in the DOOH advertising industry.