Difference between revisions of "Hide-and-Seek Privacy Challenge (NeurIPS 2020)"
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This project is about building competitive methods for NeurIPS 2020 Hide-and-Seek Privacy Challenge: | This project is about building competitive methods for NeurIPS 2020 Hide-and-Seek Privacy Challenge: | ||
Latest revision as of 11:31, 13 October 2020
| Title | Hide-and-Seek Privacy Challenge (NeurIPS 2020) |
|---|---|
| Summary | Building novel methods for privacy-preserving data sharing and/or re-identification |
| Keywords | modelling, privacy preservation, classification, re-identification |
| TimeFrame | |
| References | https://www.vanderschaar-lab.com/privacy-challenge/ |
| Prerequisites | |
| Author | |
| Supervisor | Onur Dikmen |
| Level | Master |
| Status | Open |
This project is about building competitive methods for NeurIPS 2020 Hide-and-Seek Privacy Challenge:
https://www.vanderschaar-lab.com/privacy-challenge/
Competing in this challenge would also be a challenge since the deadline is quite soon (November 15th, 2020), however submitting a reasonable method to this challenge guarantees at least a grade 4 for the thesis.
More realistically, the aim is to take up the same challenge and tackle it throughout your thesis. It provides a great platform with freely accessible dataset and open-source contributions. An evaluation set will be available only to the contributors after the deadline, so it is advantageous to enter the competition officially.
The tasks are: - Hide: Generate synthetical data based on the original dataset so that it is similar to the real data but privacy-preserving (robust to re-identification) - Seek: Classify (re-identify) people accurately from synthetical datasets
The students who are interested in this thesis must have a strong theoretical background in statistics, probability and machine learning and high grades from corresponding courses.
