June 19, 2017

Lessons of 2016 for U.S. Election Security

The 2016 election was one of the most eventful in U.S. history. We will be debating its consequences for a long time. For those of us who pay attention to the security and reliability of elections, the 2016 election teaches some important lessons. I’ll review some of them in this post.

First, though, let’s review what has not changed. The level of election security varies considerably from place to place in the United States, depending on management, procedures, and of course technology choices. Places that rely on paperless voting systems, such as touchscreen voting machines that record votes directly in computer memories (so-called DREs), are at higher risk, because of the malleability of computer memory and the lack of an auditable record of the vote that was seen directly by the voter. Much better are systems such as precinct-count optical scan, in which the voter marks a paper ballot and feeds the ballot through an electronic scanner, and the ballot is collected in a ballot box as a record of the vote. The advantage of such a system is that a post-election audit that compares a random sample of paper ballots to the corresponding electronic records can verify with high confidence that the election results are consistent with what voters saw. Of course, you have to make the audit a routine post-election procedure.

Now, on to the lessons of 2016.

The first lesson is that nation-state adversaries may be more aggressive than we had thought. Russia took aggressive action in advance of the 2016 U.S. election, and showed signs of preparing for an attack that would disrupt or steal the election. Fortunately they did not carry out such an attack–although they did take other actions to influence the election. In the future, we will have to assume the presence of aggressive, highly capable nation-state adversaries, which we knew to be possible in principle before, but now seem more likely.

The second lesson is that we should be paying more attention to attacks that aim to undermine the legitimacy of an election rather than changing the election’s result. Election-stealing attacks have gotten most of the attention up to now–and we are still vulnerable to them in some places–but it appears that external threat actors may be more interested in attacking legitimacy.

Attacks on legitimacy could take several forms. An attacker could disrupt the operation of the election, for example, by corrupting voter registration databases so there is uncertainty about whether the correct people were allowed to vote. They could interfere with post-election tallying processes, so that incorrect results were reported–an attack that might have the intended effect even if the results were eventually corrected. Or the attacker might fabricate evidence of an attack, and release the false evidence after the election.

Legitimacy attacks could be easier to carry out than election-stealing attacks, as well. For one thing, a legitimacy attacker will typically want the attack to be discovered, although they might want to avoid having the culprit identified. By contrast, an election-stealing attack must avoid detection in order to succeed. (If detected, it might function as a legitimacy attack.)

The good news is that steps like adopting auditable paper ballots and conducting routine post-election audits are useful against both election-stealing and legitimacy attacks. If we have strong evidence of voter intent, this will make election-stealing harder, and it will make falsified evidence of election-stealing less plausible. But attacks that aim to disrupt the election process may require different types of defenses.

One thing is certain: election workers have a very difficult job, and they need all of the help they can get, from the best technology to the best procedures, if we are going to reach the level of security we need.

Web Census Notebook: A new tool for studying web privacy

As part of the Web Transparency and Accountability Project, we’ve been visiting the web’s top 1 million sites every month using our open-source privacy measurement tool OpenWPM. This has led to numerous worrying findings such as the systematic abuse of newly introduced web features for fingerprinting, leading to better privacy tools and occasionally strong responses from browser vendors.

Enabling research is great — OpenWPM has led to 14 papers so far — but research is slow and requires expertise. To make our work more directly useful, today we’re announcing a new tool to study web privacy: a Jupyter notebook interface and a set of libraries to quickly answer most questions about web tracking by querying the the 500 GB of data we collect every month.

Jupyter notebook is an intuitive tool for data analysis using Python, and it’s what we use here internally for much of our own research. Notebooks are accessible with a simple web interface; yet the code, data, and memory persists on the server if you close the browser and return to it later (even from a different device). Notebooks combine code with visualizations, making them ideal for data exploration and analysis.

Who could benefit from this tool? We envision uses such as these:

  • Publishers could use our data to understand third-party tracking on their own websites.
  • Journalists could use our data to investigate and expose privacy-infringing practices.
  • Regulators and enforcement agencies could use our tool in investigations.
  • Creators of browser privacy tools could use our data to test their effectiveness.

Let’s look at an example that shows the feel of the interface. The code below computes the average number of embedded trackers on the top 100 websites in various categories such as “news” and “shopping”. It is intuitive and succinct. Without our interface, not only would the SQL version of this query be much more cumbersome, but it would require a ton of legwork and setup to even get to a point where you can write the query. Now you just need to point your browser at our notebook.

    for category, domains in census.first_parties.alexa_categories.items():
        avg = sum(1 for first_party in domains[:100]
                    for third_party in first_party.third_party_resources
                    if third_party.is_tracker) / 100
        print("Average number of trackers on %s sites: %.1f" % (category, avg))

The results confirm our finding that news sites have the most trackers, and adult sites the least. [1]

Here’s what happens behind the scenes:

  • census is a Python object that exposes all the relationships between websites and third parties as object attributes, hiding the messy details of the underlying database schema. Each first party is represented by a FirstParty object that gives access to each third-party resource (URI object) on the first party, and the ThirdParty that the URI belongs to. When the objects are accessed, they are instantiated automatically by querying the database.
  • census.first_parties is a container of FirstParty objects ordered by Alexa traffic rank, so you can easily analyze the top sites, or sites in the long tail, or specific sites. You can also easily slice the sites by category: in the example above, we iterate through each category of census.first_parties.alexa_categories.
  • There’s a fair bit of logic that goes into analyzing the crawl data which third parties are embedded on which websites, and cross-referencing that with tracking-protection lists to figure out which of those are trackers. This work is already done for you, and exposed via attributes such as ThirdParty.is_tracker.

Since the notebooks run on our server, we expect to be able to support only a limited number (a few dozen) at this point, so you need to apply for access. The tool is currently in beta as we smooth out rough edges and add features, but it is usable and useful. Of course, you’re welcome to run the notebook on your own server — the underlying crawl datasets are public, and we’ll release the code behind the notebooks soon. We hope you find this of use to you, and we welcome your feedback.

 

[1] The linked graph from our paper measures the number of distinct domains whereas the query above counts every instance of every tracker. The trends are the same in both cases, but the numbers are different. Here’s the output of the query:

 

Average number of third party trackers on computers sites: 41.0
Average number of third party trackers on regional sites: 68.8
Average number of third party trackers on recreation sites: 58.2
Average number of third party trackers on health sites: 38.4
Average number of third party trackers on news sites: 151.2
Average number of third party trackers on business sites: 55.0
Average number of third party trackers on kids_and_teens sites: 74.8
Average number of third party trackers on home sites: 94.5
Average number of third party trackers on arts sites: 108.6
Average number of third party trackers on sports sites: 86.6
Average number of third party trackers on reference sites: 43.8
Average number of third party trackers on science sites: 43.1
Average number of third party trackers on society sites: 73.5
Average number of third party trackers on shopping sites: 53.1
Average number of third party trackers on adult sites: 16.8
Average number of third party trackers on games sites: 70.5

Breaking, fixing, and extending zero-knowledge contingent payments

The problem of fair exchange arises often in business transactions — especially when those transactions are conducted anonymously over the internet. Alice would like to buy a widget from Bob, but there’s a circular problem: Alice refuses to pay Bob until she receives the widget whereas Bob refuses to send Alice the widget until he receives payment.

In a previous post, we described the fair-exchange problem and solutions for buying physical goods using Bitcoin. Today is the first of two posts in which we focus on purchasing digital goods and services. In a new paper together with researchers from City University of New York and IMDEA Software Institute, Madrid, we show that Zero-knowledge contingent payments (ZKCP), a well known protocol for the fair exchange of digital goods over the blockchain is insecure in its current form. We show how to fix ZKCP, and also extend it to a new class of problems. [Read more…]