Date: Fri, 06 Nov 2020 16:13:30 +0000
<div class="p-rich_text_section"><span style="font-size: 10pt;">Simson Garfinkel, Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau, discusses his work modernizing the Census Bureau disclosure avoidance system from private to public disclosure avoidance techniques using differential privacy. <span> Some of the discussion revolves around the topics in the paper</span> <a href="https://arxiv.org/abs/2009.03777">Randomness Concerns When Deploying Differential Privacy</a><span>.</span></span></div> <div class="p-rich_text_section"> </div> <div class="p-rich_text_section"> <p><strong>WORKS MENTIONED:</strong></p> <ul> <li>“<a href="https://link.springer.com/chapter/10.1007/11681878_14">Calibrating Noise to Sensitivity in Private Data Analysis</a>” by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith</li> <li>"<a href="https://dl.acm.org/doi/10.1145/3267323.3268949">Issues Encountered Deploying Differential Privacy</a><span>" by Simson L Garfinkel, John M Abowd, and Sarah Powazek</span></li> <li>"<a href="https://arxiv.org/abs/2009.03777">Randomness Concerns When Deploying Differential Privacy</a><span>" by Simson L. Garfinkel and Philip Leclerc </span></li> </ul> <p><br /> Check out: https://simson.net/page/Differential_privacy</p> <p><br /> Thank you to our sponsor, BetterHelp. Professional and confidential in-app counseling for everyone. Save 10% on your first month of services with www.betterhelp.com/dataskeptic</p> </div>
