"Wanting it badly is not enough" could be the title of a postmortem on the century's tech-policy battles. Think of the crypto wars: yeah, it would be super cool if we had ciphers that worked perfectly except when "bad guys" used them, but that's not ever going to happen.
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Another area is anonymization of large data-sets. There are undeniably cool implications for a system that allows us to gather and analyze lots of data on how people interact with each other and their environments without compromising their privacy.
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But "cool" isn't the same as "possible" because wanting it badly is not enough. In the mid-2010s, privacy legislation started to gain real momentum, and privacy regulators found themselves called upon to craft compromises to pass important new privacy laws.
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Those compromises took the form of "anonymized data" carve-outs, leading to the passage of laws like the #GDPR, which strictly regulated processing "personally identifying information" but was a virtual free-for-all for "de-identified" data that had been "anonymized."
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There was just one teensy problem with this compromise: de-identifying data is REALLY hard, and it only gets harder over time. Say the NHS releases prescribing data: date, doctor, prescription, and a random identifier. That's a super-useful data-set for medical research.
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And say the next year, Addison-Lee or another large minicab company suffers a breach (no human language contains the phrase "as secure as minicab IT") that contains many of the patients' journeys that resulted in that prescription-writing.
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Merge those two data-sets and you re-identify many of the patients in the data. Subsequent releases and breaches compound the problem, and there's nothing the NHS can do to either predict or prevent a breach by a minicab company.
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Even if the NHS is confident in its anonymization, it can never be confident in the sturdiness of that anonymity over time.
Worse: the NHS really CAN'T be confident in its anonymization. Time and again, academics have shown that anonymized data from the start.
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Re-identification attacks are subtle, varied, and very, very hard to defend against:
When this pointed out to the (admittedly hard-working and torn) privacy regulators, they largely shrugged their shoulders and expressed a groundless faith that somehow this would be fixed in the future. Privacy should not be a faith-based initiative.
Today, we continue to see the planned releases of large datasets with assurances that they have been anonymized. It's common for terms of service to include your "consent" to have your data shared once it has been de-identified. This is a meaningless proposition.
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To show just how easy re-identification can be, researchers at Imperial College and the Université catholique de Louvain have released The Observatory of Anonymity, a web-app that shows you how easily you can be identified in a data-set.
Feed the app your country and region, birthdate, gender, employment and education status and it tells you how many people share those characteristics. For example, my identifiers boil down to a 1-in-3 chance of being identified.
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(Don't worry: all these calculations are done in your browser and the Observatory doesn't send any of your data to a server)
If anything, The Observatory is generous to anonymization proponents. "Anonymized" data often include identifiers like the first half of a post-code.
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You can read more about The Observatory's methods in the accompanying @nature paper, "Estimating the success of re-identifications in incomplete datasets using generative models."
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As fascism burns across America, it's important to remember that Trump and his policies are *not popular*.
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Sure, the racism and cruelty excites a minority of (very broken) people, but every component of the Trump agenda is *extremely* unpopular with the American people, from tax cuts for billionaires to kidnapping our neighbors and shipping them to concentration camps.
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If there's one are where tech has shown a consistent aptitude for innovation, it's in accounting tricks that make money-losing companies appear wildly profitable. And AI is the greatest innovator of all (when it comes to accounting gimmicks).
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Since the dotcom era, tech companies have boasted about giving stuff away but "making it up in volume," inventing an ever-sweatier collection of shell-games that let them hide the business's true profit and loss.
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In 2014, I read a political science paper that nearly convinced me to quit my lifelong career as an activist: "Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens," published in *Perspectives on Politics*:
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The paper's authors are Martin Gilens, a UCLA professor of Public Policy; and Northwestern's Benjamin Page, a professor of Decision Making. Gilens and Page studied a representative sample of 1,779 policy issues.
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When a company sells you something for $2 that someone else can buy for $1, they're revaluing the dollars in your pocket at half the rate of the other guy's.
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Economists praise "price discrimination" as "efficient." That's when a company charges different customers different amounts based on inferences about their willingness to pay.
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Private equity firms are the demon princes of the hellspace that is the imploding, life-destroying, plutocrat-generating American economy.
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If you'd like an essay-formatted version of this thread to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
Their favorite scam, the "leveraged buyout" is a mafia bustout dressed up in respectable clothes, and if you mourn a beloved, failed business, chances are that an LBO was the murder weapon, and PE was the killer:
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> One actual therapist is just having ten chat GPT windows open where they just like have five seconds to interrupt the chatGPT. They have to scan them all and see if it says something really inappropriate. That's your job, to stop it.
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