February 1, 2023

Archives for June 2011

Supreme Court Takes Important GPS Tracking Case

This morning, the Supreme Court agreed to hear an appeal next term of United States v. Jones (formerly United States v. Maynard), a case in which the D.C. Circuit Court of Appeals suppressed evidence of a criminal defendant’s travels around town, which the police collected using a tracking device they attached to his car. For more background on the case, consult the original opinion and Orin Kerr’s previous discussions about the case.

No matter what the Court says or holds, this case will probably prove to be a landmark. Watch it closely.

(1) Even if the Court says nothing else, it will face the constitutionally of the use by police of tracking beepers to follow criminal suspects. In a pair of cases from the mid-1980’s, the Court held that the police did not need a warrant to use a tracking beeper to follow a car around on public, city streets (Knotts) but did need a warrant to follow a beeper that was moved indoors (Karo) because it “reveal[ed] a critical fact about the interior of the premises.” By direct application of these cases, the warrantless tracking in Jones seems constitutional, because it was restricted to movement on public, city streets.

Not so fast, said the D.C. Circuit. In Jones, the police tracked the vehicle 24 hours a day for four weeks. Citing the “mosaic theory often invoked by the Government in cases involving national security information,” the Court held that the whole can sometimes be more than the parts. Tracking a car continuously for a month is constitutionally different in kind not just degree from tracking a car along a single trip. This is a new approach to the Fourth Amendment, one arguably at odds with opinions from other Courts of Appeal.

(2) This case gives the Court the opportunity to speak generally about the Fourth Amendment and location privacy. Depending on what it says, it may provide hints for lower courts struggling with the government’s use of cell phone location information, for example.

(3) For support of its embrace of the mosaic theory, the D.C. Circuit cited a 1989 Supreme Court case, U.S. Department of Justice v. National Reporters Committee. In this case, which involved the Freedom of Information Act (FOIA) not the Fourth Amendment, the Court allowed the FBI to refuse to release compiled “rap sheets” about organized crime suspects, even though the rap sheets were compiled mostly from “public” information obtainable from courthouse records. In agreeing that the rap sheets nevertheless fell within a “personal privacy” exemption from FOIA, the Court embraced, for the first time, the idea that the whole may be worth more than the parts. The Court noted the difference “between scattered disclosure of the bits of information contained in a rap-sheet and revelation of the rap-sheet as a whole,” and found a “vast difference between the public records that might be found after a diligent search of courthouse files, county archives, and local police stations throughout the country and a computerized summary located in a single clearinghouse of information.” (FtT readers will see the parallels to the debates on this blog about PACER and RECAP.) In summary, it found that “practical obscurity” could amount to privacy.

Practical obscurity is an idea that hasn’t gotten much traction in the Courts since National Reporters Committee. But it is an idea well-loved by many privacy scholars, including myself, for whom it helps explain their concerns about the privacy implications of data aggregation and mining of supposedly “public” data.

The Court, of course, may choose a narrow route for affirming or reversing the D.C. Circuit. But if it instead speaks broadly or categorically about the viability of practical obscurity as a legal theory, this case might set a standard that we will be debating for years to come.

What Gets Redacted in Pacer?

In my research on privacy problems in PACER, I spent a lot of time examining PACER documents. In addition to researching the problem of “bad” redactions, I was also interested in learning about the pattern of redactions generally. To this end, my software looked for two redaction styles. One is the “black rectangle” redaction method I described in my previous post. This method sometimes fails, but most of these redactions were done successfully. The more common method (around two-thirds of all redactions) involves replacing sensitive information with strings of XXs.

Out of the 1.8 million documents it scanned, my software identified around 11,000 documents that appeared to have redactions. Many of them could be classified automatically (for example “123-45-xxxx” is clearly a redacted Social Security number, and “Exxon” is a false positive) but I examined several thousand by hand.

Here is the distribution of the redacted documents I found.

Type of Sensitive Information No. of Documents
Social Security number 4315
Bank or other account number 675
Address 449
Trade secret 419
Date of birth 290
Unique identifier other than SSN 216
Name of person 129
Phone, email, IP address 60
National security related 26
Health information 24
Miscellaneous 68
Total 6208

To reiterate the point I made in my last post, I didn’t have access to a random sample of the PACER corpus, so we should be cautious about drawing any precise conclusions about the distribution of redacted information in the entire PACER corpus.

Still, I think we can draw some interesting conclusions from these statistics. It’s reasonable to assume that the distribution of redacted sensitive information is similar to the distribution of sensitive information in general. That is, assuming that parties who redact documents do a decent job, this list gives us a (very rough) idea of what kinds of sensitive information can be found in PACER documents.

The most obvious lesson from these statistics is that Social Security numbers are by far the most common type of redacted information in PACER. This is good news, since it’s relatively easy to build software to automatically detect and redact Social Security numbers.

Another interesting case is the “address” category. Almost all of the redacted items in this category—393 out of 449—appear in the District of Columbia District. Many of the documents relate to search warrants and police reports, often in connection with drug cases. I don’t know if the high rate of redaction reflects the different mix of cases in the DC District, or an idiosyncratic redaction policy voluntarily pursued by the courts and/or the DC police but not by officials in other districts. It’s worth noting that the redaction of addresses doesn’t appear to be required by the federal redaction rules.

Finally, there’s the category of “trade secrets,” which is a catch-all term I used for documents whose redactions appear to be confidential business information. Private businesses may have a strong interest in keeping this information confidential, but the public interest in such secrecy here is less clear.

To summarize, out of 6208 redacted documents, there are 4315 Social Security that can be redacted automatically by machine, 449 addresses whose redaction doesn’t seem to be required by the rules of procedure, and 419 “trade secrets” whose release will typically only harm the party who fails to redact it.

That leaves around 1000 documents that would expose risky confidential information if not properly redacted, or about 0.05 percent of the 1.8 million documents I started with. A thousand documents is worth taking seriously (especially given that there are likely to be tens of thousands in the full PACER corpus). The courts should take additional steps to monitor compliance with the redaction rules and sanction parties who fail to comply with them, and they should explore techniques to automate the detection of redaction failures in these categories.

But at the same time, a sense of perspective is important. This tiny fraction of PACER documents with confidential information in them is a cause for concern, but it probably isn’t a good reason to limit public access to the roughly 99.9 percent of documents that contain no sensitive information and may be of significant benefit to the public.

Thanks again to Carl Malamud and Public.Resource.Org for their support of my research.

Universities in Brazil are too closed to the world, and that's bad for innovation

When Brazilian president Dilma Roussef visited China in the beginning of May, she came back with some good news (maybe too good to be entirely true). Among them, the announcement that Foxconn, the largest maker of electronic components, will invest US$12 billion to open a large industrial plant in the country. The goal is to produce iPads and other key electronic components locally.

The announcement was praised, and made it quickly to the headlines of all major newspapers. There is certainly reason for excitement. Brazil lost important waves of economic development, including industrialization (which only really happened in the 1940´s), or the semiconductor wave, an industry that has shown but a few signs of development in the country until now. (continue reading)

Deceptive Assurances of Privacy?

Earlier this week, Facebook expanded the roll-out of its facial recognition software to tag people in photos uploaded to the social networking site. Many observers and regulators responded with privacy concerns; EFF offered a video showing users how to opt-out.

Tim O’Reilly, however, takes a different tack:

Face recognition is here to stay. My question is whether to pretend that it doesn’t exist, and leave its use to government agencies, repressive regimes, marketing data mining firms, insurance companies, and other monolithic entities, or whether to come to grips with it as a society by making it commonplace and useful, figuring out the downsides, and regulating those downsides.

…We need to move away from a Maginot-line like approach where we try to put up walls to keep information from leaking out, and instead assume that most things that used to be private are now knowable via various forms of data mining. Once we do that, we start to engage in a question of what uses are permitted, and what uses are not.

O’Reilly’s point –and face-recognition technology — is bigger than Facebook. Even if Facebook swore off the technology tomorrow, it would be out there, and likely used against us unless regulated. Yet we can’t decide on the proper scope of regulation without understanding the technology and its social implications.

By taking these latent capabilities (Riya was demonstrating them years ago; the NSA probably had them decades earlier) and making them visible, Facebook gives us more feedback on the privacy consequences of the tech. If part of that feedback is “ick, creepy” or worse, we should feed that into regulation for the technology’s use everywhere, not just in Facebook’s interface. Merely hiding the feature in the interface, while leaving it active in the background would be deceptive: it would give us a false assurance of privacy. For all its blundering, Facebook seems to be blundering in the right direction now.

Compare the furor around Dropbox’s disclosure “clarification”. Dropbox had claimed that “All files stored on Dropbox servers are encrypted (AES-256) and are inaccessible without your account password,” but recently updated that to the weaker assertion: “Like most online services, we have a small number of employees who must be able to access user data for the reasons stated in our privacy policy (e.g., when legally required to do so).” Dropbox had signaled “encrypted”: absolutely private, when it meant only relatively private. Users who acted on the assurance of complete secrecy were deceived; now those who know the true level of relative secrecy can update their assumptions and adapt behavior more appropriately.

Privacy-invasive technology and the limits of privacy-protection should be visible. Visibility feeds more and better-controlled experiments to help us understand the scope of privacy, publicity, and the space in between (which Woody Hartzog and Fred Stutzman call “obscurity” in a very helpful draft). Then, we should implement privacy rules uniformly to reinforce our social choices.

New Research Result: Bubble Forms Not So Anonymous

Today, Joe Calandrino, Ed Felten and I are releasing a new result regarding the anonymity of fill-in-the-bubble forms. These forms, popular for their use with standardized tests, require respondents to select answer choices by filling in a corresponding bubble. Contradicting a widespread implicit assumption, we show that individuals create distinctive marks on these forms, allowing use of the marks as a biometric. Using a sample of 92 surveys, we show that an individual’s markings enable unique re-identification within the sample set more than half of the time. The potential impact of this work is as diverse as use of the forms themselves, ranging from cheating detection on standardized tests to identifying the individuals behind “anonymous” surveys or election ballots.

If you’ve taken a standardized test or voted in a recent election, you’ve likely used a bubble form. Filling in a bubble doesn’t provide much room for inadvertent variation. As a result, the marks on these forms superficially appear to be largely identical, and minor differences may look random and not replicable. Nevertheless, our work suggests that individuals may complete bubbles in a sufficiently distinctive and consistent manner to allow re-identification. Consider the following bubbles from two different individuals:

These individuals have visibly different stroke directions, suggesting a means of distinguishing between both individuals. While variation between bubbles may be limited, stroke direction and other subtle features permit differentiation between respondents. If we can learn an individual’s characteristic features, we may use those features to identify that individual’s forms in the future.

To test the limits of our analysis approach, we obtained a set of 92 surveys and extracted 20 bubbles from each of those surveys. We set aside 8 bubbles per survey to test our identification accuracy and trained our model on the remaining 12 bubbles per survey. Using image processing techniques, we identified the unique characteristics of each training bubble and trained a classifier to distinguish between the surveys’ respondents. We applied this classifier to the remaining test bubbles from a respondent. The classifier orders the candidate respondents based on the perceived likelihood that they created the test markings. We repeated this test for each of the 92 respondents, recording where the correct respondent fell in the classifier’s ordered list of candidate respondents.

If bubble marking patterns were completely random, a classifier could do no better than randomly guessing a test set’s creator, with an expected accuracy of 1/92 ? 1%. Our classifier achieves over 51% accuracy. The classifier is rarely far off: the correct answer falls in the classifier’s top three guesses 75% of the time (vs. 3% for random guessing) and its top ten guesses more than 92% of the time (vs. 11% for random guessing). We conducted a number of additional experiments exploring the information available from marked bubbles and potential uses of that information. See our paper for details.

Additional testing—particularly using forms completed at different times—is necessary to assess the real-world impact of this work. Nevertheless, the strength of these preliminary results suggests both positive and negative implications depending on the application. For standardized tests, the potential impact is largely positive. Imagine that a student takes a standardized test, performs poorly, and pays someone to repeat the test on his behalf. Comparing the bubble marks on both answer sheets could provide evidence of such cheating. A similar approach could detect third-party modification of certain answers on a single test.

The possible impact on elections using optical scan ballots is more mixed. One positive use is to detect ballot box stuffing—our methods could help identify whether someone replaced a subset of the legitimate ballots with a set of fraudulent ballots completed by herself. On the other hand, our approach could help an adversary with access to the physical ballots or scans of them to undermine ballot secrecy. Suppose an unscrupulous employer uses a bubble form employment application. That employer could test the markings against ballots from an employee’s jurisdiction to locate the employee’s ballot. This threat is more realistic in jurisdictions that release scans of ballots.

Appropriate mitigation of this issue is somewhat application specific. One option is to treat surveys and ballots as if they contain identifying information and avoid releasing them more widely than necessary. Alternatively, modifying the forms to mask marked bubbles can remove identifying information but, among other risks, may remove evidence of respondent intent. Any application demanding anonymity requires careful consideration of options for preventing creation or disclosure of identifying information. Election officials in particular should carefully examine trade-offs and mitigation techniques if releasing ballot scans.

This work provides another example in which implicit assumptions resulted in a failure to recognize a link between the output of a system (in this case, bubble forms or their scans) and potentially sensitive input (the choices made by individuals completing the forms). Joe discussed a similar link between recommendations and underlying user transactions two weeks ago. As technologies advance or new functionality is added to systems, we must explicitly re-evaluate these connections. The release of scanned forms combined with advances in image analysis raises the possibility that individuals may inadvertently tie themselves to their choices merely by how they complete bubbles. Identifying such connections is a critical first step in exploiting their positive uses and mitigating negative ones.

This work will be presented at the 2011 USENIX Security Symposium in August.