https://mw.hh.se/caisr/api.php?action=feedcontributions&feedformat=atom&user=Onur 2026-04-04T08:41:07Z User contributions MediaWiki 1.35.13 https://mw.hh.se/caisr/index.php?title=Hide-and-Seek_Privacy_Challenge_(NeurIPS_2020)&diff=4714 2020-10-13T10:31:00Z

<p>Onur: </p> <hr /> <div>{{StudentProjectTemplate<br /> |Summary=Building novel methods for privacy-preserving data sharing and/or re-identification<br /> |Keywords=modelling, privacy preservation, classification, re-identification<br /> |References=https://www.vanderschaar-lab.com/privacy-challenge/<br /> |Supervisor=Onur Dikmen<br /> |Level=Master<br /> |Status=Open<br /> }}<br /> This project is about building competitive methods for NeurIPS 2020 Hide-and-Seek Privacy Challenge:<br /> https://www.vanderschaar-lab.com/privacy-challenge/<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> The tasks are:<br /> - 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)<br /> - Seek: Classify (re-identify) people accurately from synthetical datasets<br /> <br /> 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.</div>

Onur https://mw.hh.se/caisr/index.php?title=Hide-and-Seek_Privacy_Challenge_(NeurIPS_2020)&diff=4712 2020-10-13T10:04:36Z

<p>Onur: Created page with &quot;{{StudentProjectTemplate |Summary=Building novel methods for privacy-preserving data sharing and/or re-identification |Keywords=modelling, privacy preservation, classification...&quot;</p> <hr /> <div>{{StudentProjectTemplate<br /> |Summary=Building novel methods for privacy-preserving data sharing and/or re-identification<br /> |Keywords=modelling, privacy preservation, classification, re-identification<br /> |References=https://www.vanderschaar-lab.com/privacy-challenge/<br /> |Supervisor=Onur Dikmen<br /> }}<br /> This project is about building competitive methods for NeurIPS 2020 Hide-and-Seek Privacy Challenge:<br /> https://www.vanderschaar-lab.com/privacy-challenge/<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> The tasks are:<br /> - 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)<br /> - Seek: Classify (re-identify) people accurately from synthetical datasets<br /> <br /> 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.</div>

Onur