Entries Tagged "algorithms"

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Lessons Learned from the Estonian National ID Security Flaw

Estonia recently suffered a major flaw in the security of their national ID card. This article discusses the fix and the lessons learned from the incident:

In the future, the infrastructure dependency on one digital identity platform must be decreased, the use of several alternatives must be encouraged and promoted. In addition, the update and replacement capacity, both remote and physical, should be increased. We also recommend the government to procure the readiness to act fast in force majeure situations from the eID providers.. While deciding on the new eID platforms, the need to replace cryptographic primitives must be taken into account — particularly the possibility of the need to replace algorithms with those that are not even in existence yet.

Posted on December 18, 2017 at 6:08 AMView Comments

Whistleblower Investigative Report on NSA Suite B Cryptography

The NSA has been abandoning secret and proprietary cryptographic algorithms in favor of commercial public algorithms, generally known as “Suite B.” In 2010, an NSA employee filed some sort of whistleblower complaint, alleging that this move is both insecure and wasteful. The US DoD Inspector General investigated and wrote a report in 2011.

The report — slightly redacted and declassified — found that there was no wrongdoing. But the report is an interesting window into the NSA’s system of algorithm selection and testing (pages 5 and 6), as well as how they investigate whistleblower complaints.

Posted on November 9, 2016 at 12:00 PMView Comments

Frequent Password Changes Is a Bad Security Idea

I’ve been saying for years that it’s bad security advice, that it encourages poor passwords. Lorrie Cranor, now the FTC’s chief technologist, agrees:

By studying the data, the researchers identified common techniques account holders used when they were required to change passwords. A password like “tarheels#1”, for instance (excluding the quotation marks) frequently became “tArheels#1” after the first change, “taRheels#1” on the second change and so on. Or it might be changed to “tarheels#11” on the first change and “tarheels#111” on the second. Another common technique was to substitute a digit to make it “tarheels#2”, “tarheels#3”, and so on.

“The UNC researchers said if people have to change their passwords every 90 days, they tend to use a pattern and they do what we call a transformation,” Cranor explained. “They take their old passwords, they change it in some small way, and they come up with a new password.”

The researchers used the transformations they uncovered to develop algorithms that were able to predict changes with great accuracy. Then they simulated real-world cracking to see how well they performed. In online attacks, in which attackers try to make as many guesses as possible before the targeted network locks them out, the algorithm cracked 17 percent of the accounts in fewer than five attempts. In offline attacks performed on the recovered hashes using superfast computers, 41 percent of the changed passwords were cracked within three seconds.

That data refers to this study.

My advice for choosing a secure password is here.

Posted on August 5, 2016 at 7:53 AMView Comments

The Fallibility of DNA Evidence

This is a good summary article on the fallibility of DNA evidence. Most interesting to me are the parts on the proprietary algorithms used in DNA matching:

William Thompson points out that Perlin has declined to make public the algorithm that drives the program. “You do have a black-box situation happening here,” Thompson told me. “The data go in, and out comes the solution, and we’re not fully informed of what happened in between.”

Last year, at a murder trial in Pennsylvania where TrueAllele evidence had been introduced, defense attorneys demanded that Perlin turn over the source code for his software, noting that “without it, [the defendant] will be unable to determine if TrueAllele does what Dr. Perlin claims it does.” The judge denied the request.

[…]

When I interviewed Perlin at Cybergenetics headquarters, I raised the matter of transparency. He was visibly annoyed. He noted that he’d published detailed papers on the theory behind TrueAllele, and filed patent applications, too: “We have disclosed not the trade secrets of the source code or the engineering details, but the basic math.”

It’s the same problem as any biometric: we need to know the rates of both false positives and false negatives. And if these algorithms are being used to determine guilt, we have a right to examine them.

EDITED TO ADD (6/13): Three more articles.

Posted on May 31, 2016 at 1:04 PMView Comments

NIST Starts Planning for Post-Quantum Cryptography

Last year, the NSA announced its plans for transitioning to cryptography that is resistant to a quantum computer. Now, it’s NIST’s turn. Its just-released report talks about the importance of algorithm agility and quantum resistance. Sometime soon, it’s going to have a competition for quantum-resistant public-key algorithms:

Creating those newer, safer algorithms is the longer-term goal, Moody says. A key part of this effort will be an open collaboration with the public, which will be invited to devise and vet cryptographic methods that — to the best of experts’ knowledge — ­will be resistant to quantum attack. NIST plans to launch this collaboration formally sometime in the next few months, but in general, Moody says it will resemble past competitions such as the one for developing the SHA-3 hash algorithm, used in part for authenticating digital messages.

“It will be a long process involving public vetting of quantum-resistant algorithms,” Moody said. “And we’re not expecting to have just one winner. There are several systems in use that could be broken by a quantum computer­ — public-key encryption and digital signatures, to take two examples­ — and we will need different solutions for each of those systems.”

The report rightly states that we’re okay in the symmetric cryptography world; the key lengths are long enough.

This is an excellent development. NIST has done an excellent job with their previous cryptographic standards, giving us a couple of good, strong, well-reviewed, and patent-free algorithms. I have no doubt this process will be equally excellent. (If NIST is keeping a list, aside from post-quantum public-key algorithms, I would like to see competitions for a larger-block-size block cipher and a super-fast stream cipher as well.)

Two news articles.

Posted on May 9, 2016 at 6:19 AMView Comments

New NIST Encryption Guidelines

NIST has published a draft of their new standard for encryption use: “NIST Special Publication 800-175B, Guideline for Using Cryptographic Standards in the Federal Government: Cryptographic Mechanisms.” In it, the Escrowed Encryption Standard from the 1990s, FIPS-185, is no longer certified. And Skipjack, NSA’s symmetric algorithm from the same period, will no longer be certified.

I see nothing sinister about decertifying Skipjack. In a world of faster computers and post-quantum thinking, an 80-bit key and 64-bit block no longer cut it.

ETA: My essays from 1998 on Skipjack and KEA.

Posted on March 17, 2016 at 9:54 AMView Comments

Replacing Judgment with Algorithms

China is considering a new “social credit” system, designed to rate everyone’s trustworthiness. Many fear that it will become a tool of social control — but in reality it has a lot in common with the algorithms and systems that score and classify us all every day.

Human judgment is being replaced by automatic algorithms, and that brings with it both enormous benefits and risks. The technology is enabling a new form of social control, sometimes deliberately and sometimes as a side effect. And as the Internet of Things ushers in an era of more sensors and more data — and more algorithms — we need to ensure that we reap the benefits while avoiding the harms.

Right now, the Chinese government is watching how companies use “social credit” scores in state-approved pilot projects. The most prominent one is Sesame Credit, and it’s much more than a financial scoring system.

Citizens are judged not only by conventional financial criteria, but by their actions and associations. Rumors abound about how this system works. Various news sites are speculating that your score will go up if you share a link from a state-sponsored news agency and go down if you post pictures of Tiananmen Square. Similarly, your score will go up if you purchase local agricultural products and down if you purchase Japanese anime. Right now the worst fears seem overblown, but could certainly come to pass in the future.

This story has spread because it’s just the sort of behavior you’d expect from the authoritarian government in China. But there’s little about the scoring systems used by Sesame Credit that’s unique to China. All of us are being categorized and judged by similar algorithms, both by companies and by governments. While the aim of these systems might not be social control, it’s often the byproduct. And if we’re not careful, the creepy results we imagine for the Chinese will be our lot as well.

Sesame Credit is largely based on a US system called FICO. That’s the system that determines your credit score. You actually have a few dozen different ones, and they determine whether you can get a mortgage, car loan or credit card, and what sorts of interest rates you’re offered. The exact algorithm is secret, but we know in general what goes into a FICO score: how much debt you have, how good you’ve been at repaying your debt, how long your credit history is and so on.

There’s nothing about your social network, but that might change. In August, Facebook was awarded a patent on using a borrower’s social network to help determine if he or she is a good credit risk. Basically, your creditworthiness becomes dependent on the creditworthiness of your friends. Associate with deadbeats, and you’re more likely to be judged as one.

Your associations can be used to judge you in other ways as well. It’s now common for employers to use social media sites to screen job applicants. This manual process is increasingly being outsourced and automated; companies like Social Intelligence, Evolv and First Advantage automatically process your social networking activity and provide hiring recommendations for employers. The dangers of this type of system — from discriminatory biases resulting from the data to an obsession with scores over more social measures — are too many.

The company Klout tried to make a business of measuring your online influence, hoping its proprietary system would become an industry standard used for things like hiring and giving out free product samples.

The US government is judging you as well. Your social media postings could get you on the terrorist watch list, affecting your ability to fly on an airplane and even get a job. In 2012, a British tourist’s tweet caused the US to deny him entry into the country. We know that the National Security Agency uses complex computer algorithms to sift through the Internet data it collects on both Americans and foreigners.

All of these systems, from Sesame Credit to the NSA’s secret algorithms, are made possible by computers and data. A couple of generations ago, you would apply for a home mortgage at a bank that knew you, and a bank manager would make a determination of your creditworthiness. Yes, the system was prone to all sorts of abuses, ranging from discrimination to an old-boy network of friends helping friends. But the system also couldn’t scale. It made no sense for a bank across the state to give you a loan, because they didn’t know you. Loans stayed local.

FICO scores changed that. Now, a computer crunches your credit history and produces a number. And you can take that number to any mortgage lender in the country. They don’t need to know you; your score is all they need to decide whether you’re trustworthy.

This score enabled the home mortgage, car loan, credit card and other lending industries to explode, but it brought with it other problems. People who don’t conform to the financial norm — having and using credit cards, for example — can have trouble getting loans when they need them. The automatic nature of the system enforces conformity.

The secrecy of the algorithms further pushes people toward conformity. If you are worried that the US government will classify you as a potential terrorist, you’re less likely to friend Muslims on Facebook. If you know that your Sesame Credit score is partly based on your not buying “subversive” products or being friends with dissidents, you’re more likely to overcompensate by not buying anything but the most innocuous books or corresponding with the most boring people.

Uber is an example of how this works. Passengers rate drivers and drivers rate passengers; both risk getting booted out of the system if their rankings get too low. This weeds out bad drivers and passengers, but also results in marginal people being blocked from the system, and everyone else trying to not make any special requests, avoid controversial conversation topics, and generally behave like good corporate citizens.

Many have documented a chilling effect among American Muslims, with them avoiding certain discussion topics lest they be taken the wrong way. Even if nothing would happen because of it, their free speech has been curtailed because of the secrecy surrounding government surveillance. How many of you are reluctant to Google “pressure cooker bomb”? How many are a bit worried that I used it in this essay?

This is what social control looks like in the Internet age. The Cold-War-era methods of undercover agents, informants living in your neighborhood, and agents provocateur is too labor-intensive and inefficient. These automatic algorithms make possible a wholly new way to enforce conformity. And by accepting algorithmic classification into our lives, we’re paving the way for the same sort of thing China plans to put into place.

It doesn’t have to be this way. We can get the benefits of automatic algorithmic systems while avoiding the dangers. It’s not even hard.

The first step is to make these algorithms public. Companies and governments both balk at this, fearing that people will deliberately try to game them, but the alternative is much worse.

The second step is for these systems to be subject to oversight and accountability. It’s already illegal for these algorithms to have discriminatory outcomes, even if they’re not deliberately designed in. This concept needs to be expanded. We as a society need to understand what we expect out of the algorithms that automatically judge us and ensure that those expectations are met.

We also need to provide manual systems for people to challenge their classifications. Automatic algorithms are going to make mistakes, whether it’s by giving us bad credit scores or flagging us as terrorists. We need the ability to clear our names if this happens, through a process that restores human judgment.

Sesame Credit sounds like a dystopia because we can easily imagine how the Chinese government can use a system like this to enforce conformity and stifle dissent. Our own systems seem safer, because we don’t believe the corporations and governments that run them are malevolent. But the dangers are inherent in the technologies. As we move into a world where we are increasingly judged by algorithms, we need to ensure that they do so fairly and properly.

This essay previously appeared on CNN.com.

Posted on January 8, 2016 at 5:21 AMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.