These talks were presented for the Privacy Day Webinar 2022 sponsored by the American Statistical Association's Committee on Privacy and Confidentiality. Link to recording. Talk 1: "The potential of privacy-preserving generative deep neural networks to support clinical data sharing" Brett Beaulieu-Jones, Harvard Medical School Abstract: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. Deep generative adversarial networks have the potential to produce synthetic data while maintaining privacy. In some cases, the synthetic data has been shown to maintain statistical properties of source data and to be indistinguishable to human experts. This raises two impor...
Medical data often contain sensitive personal information about individuals, posing significant limi...
The growing development of artificial intelligence (AI), particularly neural networks, is transformi...
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing...
Synthetic data generation is a powerful tool for privacy protection when considering public release ...
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support b...
The availability of genomic data is essential to progress in biomedical research, personalized medi...
The proliferation of data in recent years has led to the advancement and utilization of various stat...
Summary: Differential privacy allows quantifying privacy loss resulting from accession of sensitive ...
International audienceGenerating synthetic data represents an attractive solution for creating open ...
Government agencies collect and manage a wide range of ever-growing datasets. While such data has th...
This is the final version. Available on open access from SAGE Publications via the DOI in this recor...
How can we share sensitive datasets in such a way as to maximize utility while simultaneously safegu...
International audienceWe examine the feasibility of using synthetic medical data generated by GANs i...
Large-scale data processing prompts a number of important challenges, including guaranteeing that co...
Introduction Demand to access high quality data at the individual level for medical and healthcare ...
Medical data often contain sensitive personal information about individuals, posing significant limi...
The growing development of artificial intelligence (AI), particularly neural networks, is transformi...
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing...
Synthetic data generation is a powerful tool for privacy protection when considering public release ...
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support b...
The availability of genomic data is essential to progress in biomedical research, personalized medi...
The proliferation of data in recent years has led to the advancement and utilization of various stat...
Summary: Differential privacy allows quantifying privacy loss resulting from accession of sensitive ...
International audienceGenerating synthetic data represents an attractive solution for creating open ...
Government agencies collect and manage a wide range of ever-growing datasets. While such data has th...
This is the final version. Available on open access from SAGE Publications via the DOI in this recor...
How can we share sensitive datasets in such a way as to maximize utility while simultaneously safegu...
International audienceWe examine the feasibility of using synthetic medical data generated by GANs i...
Large-scale data processing prompts a number of important challenges, including guaranteeing that co...
Introduction Demand to access high quality data at the individual level for medical and healthcare ...
Medical data often contain sensitive personal information about individuals, posing significant limi...
The growing development of artificial intelligence (AI), particularly neural networks, is transformi...
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing...