I've been doing some research on #privacymode in browsers. (THREAD)
Use cases:
- To protect you from
• the next person to use your browser
• the network snooping
• the site knowing too much about your prior browsing
- For a fresh browsing context (no cookies/fingerprinting)
I then looked at the ways browsers implement their own #privacymode.
TL;DR: they're all different.
Do you know what your browser's privacy mode protects you from?
To note: I did this research on publicly available documentation. One of the things I learned: a number of browsers aren't saying much about how they operate in private mode.
(Hence the empty spots in the spreadsheet)
3/5
I worry that this variety of behaviours makes it hard for users to understand what changes when they flip that switch into #privacymode. It's hard to know what info it's safe to type into which form, for example — if you aren't clear on who might read it.
4/5
We also can't write specs for other features of the web platform with normative references on #privacymode.
In other words, we can't currently say "When in #privacymode, this feature should change in $theseWays."
I worry that's a missed opportunity for the web.
5/5
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Third-party cookies are not good for the web. They enable tracking, which involves following your activity across multiple websites. They can be helpful for use cases like login and single sign-on, or putting shopping choices into a cart —
2/
— but they can also be used to invisibly track your browsing activity across sites for surveillance or ad-targeting purposes. This hidden personal data collection hurts everyone’s privacy.
3/
@NHSCOVID19app Exposure notification: Using Apple & Google’s Exposure Notification APIs, your phone pays attention to when you’re within 2 meters of someone for more than 15 minutes.
@NHSCOVID19app Local risk scores: Using the postcode district (the first half of the postcode) that you give the app — the only info it has about you — it shows you the COVID-19 risk score for your area. This lets you plan to keep yourself safe.
We will be transforming some of our community’s best efforts to improve health and care, using applied AI.
1/4
Cancer screening and detecting results on scans, predicting load on beds or medicines, detecting patients at risk of post-op complications… The AI lab could do a lot to improve the data-driven NHS and care system we know we need.
2/4
It’s early days, but we hope to bring together clinicians, academics and AI researchers, combined with our users — patients and staff alike — to work on problems we face:
We talked today about our @NHSX missions: 1. Reducing the burden on clinicians and staff, so they can focus on patients 2. Giving people the tools to access information and services directly 3. Ensuring clinical information can be safely accessed, wherever it is needed
Today’s @NHSHackDay was our ecosystem at its best: clinicians, entrepreneurs, carers, patients, and developers all setting themselves a user need and building a prototype to fix it.
Rather than porters rushing round to get/deliver routine collections of blood & specimens, this team created a sign for each collection pt, with a “if urgent, scan this QR-code” to send the specific specimen into the urgent queue.
They built the back-end on a vehicle-delivery-problem algorithm, to create efficient routes & direct the most appropriate porter to collect an urgent specimen—and an app to show each porter where they were needed next.