We would like to pass on an opportunity that may be of interest to the Better Stick for Differential Privacy community.
Call for Papers
Despite the substantial benefits from using synthetic data, the process of synthetic data generation is still an ongoing technical challenge. Although the two scenarios of limited data and privacy concerns share similar technical challenges such as quality and fairness, they are often studied separately. We invite researchers to submit papers that discuss challenges and advances in synthetic data generation, including but not limited to the following topics.
- How can we evaluate the quality of synthetically generated datasets?
- How can we handle mixed-type datasets such as tabular data with both categorical and continuous variables?
- How can we generate synthetic samples to augment rare samples or limited labeled data?
- How can we address privacy violations, measure privacy leakage, and provide probable privacy guarantees?
- How can we retain semantic meaning of original samples in the synthetic data?
- What are the right datasets/applications/benchmarks to propel this research area forward?
- How can we measure and mitigate biases, and thereby ensure fairness in data synthesis?
Selected papers will be presented at the 1st Synthetic Data Generation workshop at ICLR 2021 on May 8, 2021.
Papers are due February 26, 2021. Selected papers will be determined and notified by March 26, 2021.
Submissions in the form of extended abstracts must be at most 4 pages long (not including references; additional supplementary material may be submitted but may be ignored by reviewers), anonymized, and adhere to the ICLR format. We encourage submissions of work that are new to the synthetic data generation community. Submissions solely based on work that has been previously published in machine learning conferences or relevant venues are not suitable for the workshop. On the other hand, we allow submission of works currently under submission and relevant works recently published in relevant venues. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have a link to arxiv or a pdf added on the workshop webpage.
Submission Link: https://cmt3.research.microsoft.com/SDGICLRW2021