hlgmos_project1_progress <h1>Membership</h1><p>The membership of the synthetic data project is now at 38.</p><h1>Scope</h1><p>The goal of the Synthetic Data Project is to <span style="color: #172b4d;">develop a hands-on guide for creating and using synthetic data primarily geared towards data protection and disclosure control. The target audience of this guide includes both managers and methodologist at NSOs to provide a starting point for using and creating synthetic data. </span></p><p><span style="color: #172b4d;">After the HLG-MOS Executive Board recommendations, the guide will emphasize creating synthetic data from non-survey data sources. In addition, the use cases will highlight the circumstances in which to synthetic data is the appropriate solution to their output privacy problems. </span></p><h1><span style="color: #172b4d;">Work Packages</span></h1><p>Work package 2 - methodology - has been under with presentations of new method from Statistics New Zealand and Australian Bureau of Statistics. </p><h1>Patnerships</h1><p>The HLG-MOS Executive Board recommended partnerships with the ML 2021 group and the Input Privacy Preserving Techniques project. I met with both projects and synthetic data can be a suitable option for testing data. </p> hlgmos_project1_next steps <h1>Work Packages</h1><p><strong>Work Package 1 - Use Cases: </strong>the BSTN Synthetic Data Working Group in 2020 had created a foundation of use cases for synthetic data. After the EB&apos;s recommendations, the use case categories are being reassessed. The use case work package has two main outputs:</p><ol><li>A description of different use cases and highlighting their analytical and disclosure risk needs</li><li>Creating a gradient of different use cases based on their analytical and disclosure risk needs</li></ol><p>The use cases will form the basis for the methodology and quality measure recommendations. </p><p>The target deadline for work package 1 is end of May 2021. </p><p><strong>Work Package 2 - methodology: </strong>The following methodology presentations have been scheduled:</p><ul style="text-align: start;"><li>March 4, 2021: Joerg Drechsler presenting on<span> </span><em>method used to synthesize US business data</em></li><li>April 1, 2021: Kenza Sallier presenting<span> </span><em>Statistics Canada’ experience creating public synthetic datasets using the FCS and the Synthpop package</em><span> </span>and Gillian Raab and Beata Nowok will be presenting on new additions to the Synthpop package. </li><li>May 6, 2021: Tentative Christine Task and Rolando Rodriguez</li></ul><p><br /></p><h1>Patnerships</h1><p>The ML 2021 group will get back to me on their requirements for testing data and we will then determined if synthetic data is the right solution. </p> DA_risks hlgmos_project2_progress hlgmos_project2_next steps bstn_1 bstn_2 bstn_3 bstn_4 bstn_5 bstn_6 bstn_7 bstn_o capcom1 capcom2 capcom3 capcom4 capcom5 capcom6 placeholder capcom8 ss1 <p>The Task Team had a sprint session in January with the aim to harmonise the work done so far and move forward with the descrption of the remaining sub-processes in a more harmonised manner. The current work is focusing on the harmonisation of sub-processes at the specification level (Phases 1, 2, 4, 5, 6) after which, authors of each sub-process example can reflect the changes at the implementation level. Divison of work for the remaining sub-processes is also currently ongoing.</p><p><br /></p> ss2 ss3 ss4 ss5 ss6 ss7 ss_o ml2021-1 <br /> ml2021-2 . ml2021-3 ml2021-4 ml2021-5 ml2021-other .