1.0CLTChttps://cltc.berkeley.eduCLTC Adminhttps://cltc.berkeley.edu/author/cltc-admin/rich600338<blockquote class="wp-embedded-content"><a href="https://cltc.berkeley.edu/grants/statistical-foundations-to-advance-provably-private-algorithms/">Statistical Foundations to Advance Provably Private Algorithms</a></blockquote> <script type='text/javascript'> <!--//--><![CDATA[//><!-- /*! This file is auto-generated */ !function(c,d){"use strict";var e=!1,n=!1;if(d.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},!c.wp.receiveEmbedMessage)if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(t)if(t.secret||t.message||t.value)if(!/[^a-zA-Z0-9]/.test(t.secret)){for(var r,a,i,s=d.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),n=d.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),o=0;o<n.length;o++)n[o].style.display="none";for(o=0;o<s.length;o++)if(r=s[o],e.source===r.contentWindow){if(r.removeAttribute("style"),"height"===t.message){if(1e3<(i=parseInt(t.value,10)))i=1e3;else if(~~i<200)i=200;r.height=i}if("link"===t.message)if(a=d.createElement("a"),i=d.createElement("a"),a.href=r.getAttribute("src"),i.href=t.value,i.host===a.host)if(d.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener("message",c.wp.receiveEmbedMessage,!1),d.addEventListener("DOMContentLoaded",t,!1),c.addEventListener("load",t,!1);function t(){if(!n){n=!0;for(var e,t,r=-1!==navigator.appVersion.indexOf("MSIE 10"),a=!!navigator.userAgent.match(/Trident.*rv:11\./),i=d.querySelectorAll("iframe.wp-embedded-content"),s=0;s<i.length;s++){if(!(e=i[s]).getAttribute("data-secret"))t=Math.random().toString(36).substr(2,10),e.src+="#?secret="+t,e.setAttribute("data-secret",t);if(r||a)(t=e.cloneNode(!0)).removeAttribute("security"),e.parentNode.replaceChild(t,e)}}}}(window,document); //--><!]]> </script><iframe sandbox="allow-scripts" security="restricted" src="https://cltc.berkeley.edu/grants/statistical-foundations-to-advance-provably-private-algorithms/embed/" width="600" height="338" title="“Statistical Foundations to Advance Provably Private Algorithms” — CLTC" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe>In the past year, several major companies have deployed data processing systems based on differential privacy. These systems use mathematical techniques to limit the information that can be learned about individuals, but a number of limitations threaten the success of this approach. This research will bring statistical techniques to bear...