jm + taxis   6

Understanding Uber: It's Not About The App
the next time you see a link to a petition or someone raging about this decision being ‘anti-innovation’, remember Greyball. Remember the Metropolitan Police letter [regarding several sexual assaults which Uber didn't report to police]. Remember that this is about holding ULL, as a company, to the same set of standards to which every other mini-cab operator in London already complies. Most of all though remember: it is not about the app.
uber  ull  safety  crime  london  assault  greyball  taxis  cabs  apps 
27 days ago by jm
Why Canada should de-activate Uber
The Uber controversy is not just—or even mainly—a technology story, it’s fundamentally a deregulation story; the story of a uniquely American fundamentalist free-market worldview being sold to us in the name of “car-sharing” and innovation.
uber  free-market  libertarian  taxis  regulation  canada  cities 
november 2014 by jm
Riding with the Stars: Passenger Privacy in the NYC Taxicab Dataset
A practical demo of "differential privacy" -- allowing public data dumps to happen without leaking privacy, using Laplace noise addition
differential-privacy  privacy  leaks  public-data  open-data  data  nyc  taxis  laplace  noise  randomness 
september 2014 by jm
173 million 2013 NYC taxi rides shared on BigQuery : bigquery
Interesting! (a) there's a subreddit for Google BigQuery, with links to interesting data sets, like this one; (b) the entire 173-million-row dataset for NYC taxi rides in 2013 is available for querying; and (c) the tip percentage histogram is cool.
datasets  bigquery  sql  google  nyc  new-york  taxis  data  big-data  histograms  tipping 
july 2014 by jm
Hailo pulling in EUR1M per month in Dublin alone
based on these (pretty rough) estimates. Good going, I'm a massive fan
hailo  taxis  driving  cars  public-transport  dublin  b2c  b2b 
june 2014 by jm
NYC generates hash-anonymised data dump, which gets reversed
There are about 1000*26**3 = 21952000 or 22M possible medallion numbers. So, by calculating the md5 hashes of all these numbers (only 24M!), one can completely deanonymise the entire data. Modern computers are fast: so fast that computing the 24M hashes took less than 2 minutes.


(via Bruce Schneier)

The better fix is a HMAC (see http://benlog.com/2008/06/19/dont-hash-secrets/ ), or just to assign opaque IDs instead of hashing.
hashing  sha1  md5  bruce-schneier  anonymization  deanonymization  security  new-york  nyc  taxis  data  big-data  hmac  keyed-hashing  salting 
june 2014 by jm

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