Of course there will be dissatisfaction from users of the data. Anyone that wants to use census data will prefer less privacy in the data. And anytime privacy is enforced the data becomes less useful. It would be certainly very convenient for both advertisers and gerrymandering political consultants to have detailed data on every citizen.
replyAs the article says anytime you want to enforce privacy, the data becomes somewhat less useful, there is just no way around that.
The point of rights is that we have them and that they should not be trampled upon when they become slightly inconvenient to someone in power.
Are you sure about that? You are saying that differentially private census data couldn't be used for gerrymeandering and advertisement while non differentially private data could? Hard to believe, I'm not an advertisement or gerrymeandering expert but I would assume people running ads or cutting up districts are mostly interested in aggregate statistics i.e. they won't care about single households? And I would assume they can rely on voter files, party databases etc... And to the contrary there are reports [1] that indicate differential privacy actually makes gerrymeandering analysis more difficult or impossible. So, not really an argument for differential privacy, discriminatory action can be equally well taken based on differentially private data as the government cares about groups not individuals and groups aren't protected by differential privacy. It seems people really fundamentally misunderstand what this technique can achieve and what it won't do.
reply1: https://pmc.ncbi.nlm.nih.gov/articles/PMC8494446/?utm_source...
> serious issues downstream as most researchers and statisticians that ingested the data weren't prepared for receiving noisy data values
replyThey weren't prepared for data that was obviously noisy. The data has always been inherently inaccurate, and folks just chose to ignore that previously
No, there are dozens of articles discussing the mechanism and explaining the impact it had in different areas e.g. [1,2,3]. And the release mechanism wasn't just "add noise", far from it, you may read the original paper [4] to see how intricate it was, anyone wanting to make real use the resulting data would have needed to understand that approach in detail to work with the resulting data. The report of the national academies [3] is probably the most comprehensive analysis of the mechanism and the complications it introduced, so writing "it has always been inherently inaccurate" is just wrong, this new mechanism was way worse than just introducing unbiased sampling noise.
reply1: https://www.aeaweb.org/articles?id=10.1257%2Fpandp.20191107&... 2: https://www.science.org/doi/10.1126/sciadv.abk3283?utm_sourc... 3: https://www.nationalacademies.org/read/27150/chapter/14