Category: privacy

Automotive Privacy

[Update: clarified a sentence about whose privacy is touched, and where.]

I had missed the story “Big Brother on wheels: Why your car company may know more about you than your spouse.” There are surprising details, including that you might be able to shut it off, and the phrase “If a customer declines, we do not collect any data from the vehicle.” I do wonder how a customer can decline — does it involve not buying a GM car?

When we did a privacy threat model at the Seattle Privacy Coalition, we found these issues. We also were surprised that the defense, taking a car driven by someone else (a taxi, or a Lyft/Uber) makes such a big difference, leaving the owner of the car associated with the trip via license plate, toll beacons, tire pressure monitors, traffic sensors, maps, and other technologies with tracking implications. And the passenger is associated if payment is by card, or the ride is booked via an app. splits/confuses the difference. It may also be that driving for Lyft/Uber acts as a defense, by classifying a car as a carshare, but it seems pretty easy to see through that to where the car is parked (especially overnight) and to repeated trips to dis-ambiguate between paid and personal rides.

Carpenter!

The decision in Carpenter v. United States is an unusually positive one for privacy. The Supreme Court ruled that the government generally can’t access historical cell-site location records without a warrant. (SCOTUS Blog links to court documents. The court put limits on the “third party” doctrine, and it will be fascinating to see how those limits play out.

A few interesting links:

As I said previously, I am thankful to the fine folks at the Knight First Amendment Institute at Columbia University for the opportunity to help with their technologists amicus brief in this case, and I’m glad to see that the third party doctrine is under stress. That doctrine has weakened the clear aims of the fourth amendment in protecting our daily lives against warrantless searches as our lives have involved storing more of our “papers” outside our homes.

Image via the mobile pc guys, who have advice about how to check your location history on Google, which is one of many places where it may be being captured. That advice might still be useful — it’s hard to tell if the UI has changed, since I had turned off those features.

“346,000 Wuhan Citizens’ Secrets” was an exhibition created with $800 worth of data by Deng Yufeng. From the New York Times:

Chinese Personal Data framed

Six months ago, Mr. Deng started buying people’s information, using the Chinese messaging app QQ to reach sellers. He said that the data was easy to find and that he paid a total of $800 for people’s names, genders, phone numbers, online shopping records, travel itineraries, license plate numbers — at a cost at just over a tenth of a penny per person.

The Personal Data of 346,000 People, Hung on a Museum Wall
,” by Sui-Lee Wee and Elsie Chen.

Citizen Threat Modeling and more data

Last week, in “Threat Modeling: Citizens Versus Systems,” I wrote:

I think that was a right call for the first project, because the secondary data flows are a can of worms, and drawing them would, frankly, look like a can of worms.
(and)

Many organizations don’t disclose them beyond saying “we share your data to deliver and improve the service,” those that do go farther disclose little about the specifics of what data is transferred to who.

Paypal Partnerships
Today, via Bruce Schneier, we see that Paypal has disclosed the list of over 600 companies they might share your data with. He rightly asks if that’s unusual. We don’t know. My instinct is that it’s not unusual for a financial multi-national.

I’m standing by the questions I asked; the first level of categories in the Paypal list may act as a good third level for our analysis. It will be interesting to see if others use the same categories. If they don’t, the analysis process is magnified.

Their categories are:

  1. Payment Processors
  2. Audit
  3. Customer Service outsourcing
  4. Credit reference and fraud agencies
  5. Financial products
  6. Commercial partnerships
  7. Marketing and public relations
  8. Operational services
  9. Group companies
  10. Commercial partners
  11. Legal
  12. Agencies

It’s unclear to me how 6 (“Commercial partnerships”) differs from 10 (“Commercial partners”). I say this because I’m curious, not to point and laugh. We should cut Paypal some slack and appreciate that this is a new process to handle a new legal requirement. I’m also curious if 12 (“agencies”) means “law enforcement agencies” or something else.

Visualization from How PayPal Shares Your Data.

Threat Modeling: Citizens Versus Systems

Recently, we shared a privacy threat model which was centered on the people of Seattle, rather than on the technologies they use.

Because of that, we had different scoping decisions than I’ve made previously. I’m working through what those scoping decisions mean.

First, we cataloged how data is being gathered. We didn’t get to “what can go wrong?” We didn’t ask about secondary uses or transfers — yet. I think that was a right call for the first project, because the secondary data flows are a can of worms, and drawing them would, frankly, look like a can of worms. We know that most of the data gathered by most of these systems is weakly protected from government agencies. Understanding what secondary data flows can happen will be quite challenging. Many organizations don’t disclose them beyond saying “we share your data to deliver and improve the service,” those that do go farther disclose little about the specifics of what data is transferred to who. So I’d like advice: how would you tackle secondary data flows?

Second, we didn’t systematically look at the question of what could go wrong. Each of those examinations could be roughly the size and effort of a product threat model. Each requires an understanding of a person’s risk profile: victims of intimate partner violence are at risk differently than immigrants. We suspect there’s models there, and working on them is a collaborative task. I’d like advice here. Are there good models of different groups and their concerns on which we could draw?

On Tuesday, I spoke at the Seattle Privacy/TechnoActivism 3rd Monday meeting, and shared some initial results from the Seattle Privacy Threat Model project.

Overall, I’m happy to say that the effort has been a success, and opens up a set of possibilities.

  • Every participant learned about threats they hadn’t previously considered. This is surprising in and of itself: there are few better-educated sets of people than those willing to commit hours of their weekends to threat modeling privacy.
  • We have a new way to contextualize the decisions we might make, evidence that we can generate these in a reasonable amount of time, and an example of that form.
  • We learned about how long it would take (a few hours to generate a good list of threats, a few hours per category to understand defenses and tradeoffs), and how to accelerate that. (We spent a while getting really deep into threat scenarios in a way that didn’t help with the all-up models.)
  • We saw how deeply and complexly mobile phones and apps play into privacy.
  • We got to some surprising results about privacy in your commute.

More at the Seattle Privacy Coalition blog, “Threat Modeling the Privacy of Seattle Residents,” including slides, whitepaper and spreadsheets full of data.

On Wednesday, the supreme court will consider whether the government must obtain a warrant before accessing the rich trove of data that cellphone providers collect about cellphone users’ movements. Among scholars and campaigners, there is broad agreement that the case could yield the most consequential privacy ruling in a generation. (“Supreme court cellphone case puts free speech – not just privacy – at risk.”)

Bruce Schneier has an article in the Washington Post, “How the Supreme Court could keep police from using your cellphone to spy on you,” as does Stephen Sachs:

The Supreme Court will hear arguments this Wednesday in Carpenter v. United States, a criminal case testing the scope of the Fourth Amendment’s right to privacy in the digital age. The government seeks to uphold Timothy Carpenter’s conviction and will rely, as did the lower court, on the court’s 1979 decision in Smith v. Maryland, a case I know well.

I argued and won Smith v. Maryland when I was Maryland’s attorney general. I believe it was correctly decided. But I also believe it has long since outlived its suitability as precedent. (“The Supreme Court’s privacy precedent is outdated.”)

I am pleased to have been able to help with an amicus brief in the case, and hope that the Supreme Court uses this opportunity to protect all of our privacy. Good luck to the litigants!

“In an amicus brief filed in the U.S. Supreme Court, leading technology experts represented by the Knight First Amendment Institute at Columbia University argue that the Fourth Amendment should be understood to prohibit the government from accessing location data tracked by cell phone providers — “cell site location information” — without a warrant.”

For more, please see “In Supreme Court Brief, Technologists Warn Against Warrantless Access to Cell Phone Location Data.” [Update: Susan Landau has a great blog post “Phones Move – and So Should the Law” in which she frames the issues at hand.]

I’m pleased to be one of the experts involved.

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