Synthetic data: Innovation for public good

 
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RSS local group meetings

Monday 01 April 2019, 12:30pm - 04:00pm

 
Location Room R103, Office for National Statistics, Newport, NP10 8XG

About the event

We would like to invite you to a joint event hosted by the RSS, ONS Data Science Campus and GSS Best Practice and Impact division on the 1st of April at the Office for National Statistics, Newport. 

The event will focus on synthetic data, bringing together experts from public sector, industry and academia to investigate and understand the challenges of privacy and synthetic data, and how innovation in this space can be used for public good. The emphasis will be on sharing knowledge, ideas and innovative research, addressing problems and discussing potential solutions with the view of developing a community of experts and facilitating discussions and knowledge exchange.

This event is following up on the challenges and opportunities identified by the National Statistician’s Quality Review on Privacy and Confidentiality published in December 2018, with synthetic data being identified as one of the key areas where further research and development is needed. 


There will be an opportunity for networking and light refreshments from 12:30 pm and the main event will begin at 1 pm. The exact agenda will be updated shortly. 

Contact: Gentiana D. Roarson (This email address is being protected from spambots. You need JavaScript enabled to view it.) or Catrin Cheung (This email address is being protected from spambots. You need JavaScript enabled to view it.). 

Attendance is free and open to all, whether fellows of the RSS or not, but pre-registration is required.

 


              

For external visitors to the ONS, please report to the main reception upon arrival, where you will be escorted to the meeting room. 

Organiser Name Gentiana D. Roarson and Catrin Cheung

Email Address This email address is being protected from spambots. You need JavaScript enabled to view it.

Organising Group(s) RSS South Wales Local Group, Data Science Campus and Best Practice and Impact division

 

 

 

 

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