For the first time since the start of his second term, #Trump hit 50% #disapproval in #VoteHub's #polling #aggregation today. https://votehub.com/polls/
Presidents have come back from this. #Reagan bottomed out at something like 30% early in his first term, and then of course won re-election in a landslide. But Trump doesn't have that kind of political acumen.
If we have anything resembling a fair #election, 2026 will be a bloodbath, and I'm willing to bet 2028 as well. If we don't, well, it will be a bloodbath of a different kind.
votehub.comPresidential Approval Aggregate & More - VoteHubTrack presidential approval, generic ballot, and more with VoteHub’s polling aggregate. Updated in real time with clear, transparent methodology.
Estimating the success of re-identifications in incomplete datasets using generative models [Nature][open access]
![A screen capture of text from a section of an online report. One section of the text has been highlighted by a viewer:
"...99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes"
The entire screen-captured text, including the highlighted section, is below:
"legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that [highlighted] 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes.[/highlighted] Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model."](https://games.ngo/system/media_attachments/files/114/093/454/204/947/385/original/49b6ef5b9fd54e4f.png "A screen capture of text from a section of an online report. One section of the text has been highlighted by a viewer:
\"...99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes\"
The entire screen-captured text, including the highlighted section, is below:
\"legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that [highlighted] 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes.[/highlighted] Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model.\"")
Hey friends, my buddy Steve Thompson PhD (
