Quantum Computing Kills Encryption

Imagine a world where the most widely-used cryptographic methods turn out to be broken: quantum computers allow encrypted Internet data transactions to become readable by anyone who happened to be listening. No more HTTPS, no more PGP. It sounds a little bit sci-fi, but that’s exactly the scenario that cryptographers interested in post-quantum crypto are working to save us from. And although the (potential) threat of quantum computing to cryptography is already well-known, this summer has seen a flurry of activity in the field, so we felt it was time for a recap.

If you take the development of serious quantum computing power as a given, all of the encryption methods based on factoring primes or doing modular exponentials, most notably RSA, elliptic curve cryptography, and Diffie-Hellman are all in trouble. Specifically, Shor’s algorithm, when applied on a quantum computer, will render the previously difficult math problems that underlie these methods trivially easy almost irrespective of chosen key length. That covers most currently used public-key crypto and the key exchange that’s used in negotiating an SSL connection. That is (or will be) bad news as those are what’s used for nearly every important encrypted transaction that touches your daily life.

All is not doom and gloom, however. There are families of public-key algorithms that aren’t solved by Shor’s algorithm or any of the other known quantum algorithms, although they haven’t been subjected to as much (classical) cryptanalysis and the algorithms and protocols aren’t as polished yet. (More on this topic below.)

Strong symmetric ciphers, algorithms that use the same key for encryption and decryption (AES, Blowfish, etc.) will also be easier to crack with quantum computers, but only by roughly a factor of two. So if you are happy with AES-128 today, you’ll be happy with AES-256 in a quantum-computing future. That’s good news for a lot of the nation’s classified documents, for instance.

But let’s just reiterate that the hollowing-out of essentially all public-key crypto would be a very bad thing.

Quantum computing is still in its infancy, but it may already be time to start worrying about your data today. As a warning sign, the NSA backed off of its previous recommendations because they weren’t robust to quantum computing attacks. For sheer Schadenfreude, read the following excerpt from this August update to their website:

For those vendors and partners that have already transitioned to Suite B [elliptic curve cryptography], we recognize that this took a great deal of effort on your part, and we thank you for your efforts. … Unfortunately, the growth of elliptic curve use has bumped up against the fact of continued progress in the research on quantum computing, which has made it clear that elliptic curve cryptography is not the long term solution many once hoped it would be. Thus, we have been obligated to update our strategy.

In other words, “sorry we got you to spend lots of money switching over to something that’s now looking to be obsolete in the near future”. So the NSA is taking the quantum threat seriously.

While it’s sweet to see the NSA publicly eat a little bit of (pre-emptive) crow, the fact that they’re afraid that quantum cryptanalysis is going to break things in the near future, but they don’t have any solid recommended alternatives yet, should scare you a little bit. Indeed, on their list of recommendations, only AES is not completely destroyed by quantum computing. That’s kinda spooky. This article sparked by NSA’s revelation at Ars Technica is a good read.

In short, it looks like the quantum computing apocalypse is coming, but there’s current research going on and hopefully we’ll get some solid procedures in place in time. Nobody really knows how long we’ve got until quantum computers will be able to handle real-world cryptanalysis, but the numbers that the post-quantum folks toss around are in the ten-to-thirty year range.

Sometime before flying cars, then. As far as Shor’s algorithm goes, the largest quantum-computer-factored multiple-of-primes is 21. (Which two primes those are is left as an exercise to the reader.) Before laughing too hard, bear in mind that five years ago the largest possible factor was fifteen, and there’s a lot of research money and brains going into the quantum computing problem.

And when the computers arrive, they’ll have a whole spate of potential algorithms waiting for them to crunch on. The Quantum Algorithm Zoo, maintained by [Stephen Jordan], a physicist at NIST, is frequently updated and takes stock of the state of the art. Looking through the list, if you think your problem is hard but it’s in the “superpolynomial” improvement category, it probably won’t be hard forever.

In early September, a group of crypto scientists working on post-quantum cryptography met up for a week-long seminar in a German mansion and put their heads together. This Nature article nicely summarizes the meeting, and if you’re interested in what you could be doing now to safeguard your data further than a couple decades into the future, the initial recommendations for post-quantum encryption systems that came out of the seminar summarize the state of the art.

In particular, McEliece cryptosystems come out looking like a good alternative to the current public-key infrastructure. Instead of relying on factoring large numbers, a McEliece system hides your data by first wrapping it up with an error-correcting code (ECC) and then deliberately adding noise to it. Quantum computers, incidentally, turn out not to be very useful in computing some types of ECCs, which is the whole point of this operation.

Normally ECCs make it easy to recover a signal that has noise added to it; they’re not “codes” in the encryption sense at all. The crucial step in McEliece cryptography is that you create the ECC with an easy-to-reverse code, and then convert the ECC into a hard-to-solve one by pre- and post-multiplying by matrices. These matrices, that turn the hard problem into the easy problem and vice-versa, become the secret key. So you take your data, add error correction, then add noise, and then make removing the noise difficult unless you’ve got the “password”.

If you’re interested in digging a little bit deeper into the methods of quantum computing and quantum-proof crypto, this mathy but still readable survey of the field from 2001 (PDF) is a great start.

Practical quantum computing is probably ten to thirty years away. When it comes, whatever you’ve encrypted using today’s standard public-key encryption systems will be trivial to decrypt. Anyone storing your (or your government’s) data now will likely be able to read it when today’s toddler is enrolling in college. And you can bet that a good part of the rationale for the NSA’s recommendation about transitioning away from susceptible technologies is that they know of some large, well-funded government agencies who are doing that storing on a large scale today.

So is changing our public-key crypto today too early or too late? The tide seems to be just turning. The risk of switching right now to a quantum-proof method like McEliece is that it’s less battle-tested than RSA, and certainly slower to implement because it hasn’t yet been über-optimized, but the costs of not switching are growing year by year. It’s like Frogger, where you’re waiting for a log with a fly on it, but you’re also scrolling off the side of the screen. We’ve probably all got to jump, but when?

Data Storytelling: The Essential Data Science Skill Everyone Needs

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google’s Chief Economist Dr. Hal R.Varian stated, "The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian’s astute exam.

Google's Chief Economist, Dr. Hal R. Varian predicted the growing importance of data skills back in... [+] 2009. (Photo by Imeh Akpanudosen/Getty Images for Variety)

As data becomes increasingly ubiquitous, companies are desperately searching for talent with these data skills. LinkedIn recently reported data analysis is one of the hottest skill categories over the past two years for recruiters, and it was the only category that consistently ranked in the top 4 across all of the countries they analyzed. Interestingly, much of the current hiring emphasis has centered on the data preparation and analysis skills—not the "last mile" skills that help convert insights into actions. Many of the heavily-recruited individuals with advanced degrees in economics, mathematics, or statistics struggle with communicating their insights to others effectively—essentially, telling the story of their numbers.

The need for more data storytellers is only going to increase in the future. With the shift towards more self-service capabilities in analytics and business intelligence, the pool of people generating insights will expand beyond just analysts and data scientists. This new breed of data tools will make it easier for people across business functions to access and explore the data on their own. As a result, we’re going to see an unprecedented number of insights being generated within companies than ever before. However, unless we can improve the communication of these insights we will also see a poorer insight-to-value conversion rate. If an insight isn’t understood and isn’t compelling, no one will act on it and no change will occur.

Data visualization expert Stephen Few said, “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” Any insight worth sharing is probably best shared as a data story. The phrase “data storytelling” has been associated with many things—data visualizations, infographics, dashboards, data presentations, and so on. Too often data storytelling is interpreted as just visualizing data effectively, however, it is much more than just creating visually-appealing data charts. Data storytelling is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative.

It’s important to understand how these different elements combine and work together in data storytelling. When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. Ample context and commentary are often needed to fully appreciate an insight. When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations.

Finally, when narrative and visuals are merged together, they can engage or even entertain an audience. It’s no surprise we collectively spend billions of dollars each year at the movies to immerse ourselves in different lives, worlds, and adventures. When you combine the right visuals and narrative with the right data, you have a data story that can influence and drive change.

Why data storytelling is essential

For thousands of years, storytelling has been an integral part of our humanity. Even in our digital age, stories continue to appeal to us just as much as they did to our ancient ancestors. Stories play a vibrant role in our daily lives—from the entertainment we consume to the experiences we share with others to what we conjure up in our dreams.

Modern-day storytelling is often associated with the popular TED conference series and its slogan of “Ideas Worth Spreading.” Analysis of the most popular 500 TED Talk presentations found that stories made up at least 65% of their content. Throughout time, storytelling has proven to be a powerful delivery mechanism for sharing insights and ideas in a way that is memorable, persuasive, and engaging.

For some people, crafting a story around the data may seem like an unnecessary, time-consuming effort. They may feel the insights or facts should be sufficient to stand on their own as long as they’re reported in a clear manner. They may believe the revealed insights alone should influence the right decisions and drive their audience to act. Unfortunately, this point of view is based on the flawed assumption that business decisions are based solely on logic and reason.

In fact, neuroscientists have confirmed decisions are often based on emotion, not logic. USC professor Antonio Damasio found patients, who had brain damage in an area that helped to process emotions (prefrontal cortex), struggled to make basic decisions when choosing between alternatives. Deciding on where to eat or when to schedule an appointment turned into lengthy cost-benefit debates for these individuals. Interestingly, these patients’ decision-making skills were significantly impaired by the lack of emotional judgment. Emotion actually plays an essential role in helping our brains to navigate the alternatives and arrive at a timely decision.

When you package up your insights as a data story, you build a bridge for your data to the influential, emotional side of the brain. When neuroscientists observed the effects detailed information had on an audience, brain scans revealed it only activated two areas of the brain associated with language processing: Broca’s area and Wernicke’s area. However, when someone is absorbed in a story, they discovered it stimulated more areas of the brain. . This subtle but important difference pays dividends for data storytellers in a few key ways:

In a previous article, I shared an account of Ignaz Semmelweis, a mid-nineteenth century obstetrician, who discovered hand washing could save countless lives but failed to communicate his findings effectively to a skeptical medical community. His data was ignored, his life-saving ideas were rejected, and he was sadly discredited by his colleagues.

Many bold, incredible insights will suffer a similar fate if they are not successfully molded into data stories. Uncovering key insights is one skill and communicating them is another—both are equally critical to deriving value from the data your business is now amassing. Data storytelling represents an exciting, new field of expertise where art and science truly converge. My hope is more data storytellers—from across an organization—will emerge to ensure the survival and adoption of more transformative insights.

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JEE Exam Main 2020: New Exam Pattern and Syllabus released, Know what are the changes at jeemain.nta.nic.in

National Testing Agency (NTA) has released an application form for the engineering entrance exam on September 2 (Monday). The interested candidates can apply at jeemain.nic.in or nta.ac.in. NTA has already started receiving applications for JEE Main Exam online, now the agency has announced the introduction of a new paper along with a new exam pattern for the JEE Main Exam.

Till 2018, JEE Main was held once in a year by the Central Board of Secondary Education, however, the NTA conducts the test twice a year now. The first session of JEE Main was held in January and the second was conducted in April, however over 10 lakh candidates are expected to appear for the exam this year. 

The Paper-I was meant for candidates aspiring to take admission in B.Tech. courses, while Paper-II was for those who wished to take admission in B.Arch. or B.Planning courses offered by NITs, IIITs or Centrally-funded Technical Institutes.

Now, the agency has introduced Paper-III which is only meant for entry to B.Planning courses, which means now students have the clear choice to appear for Paper-I meant for B.Tech., Paper-II for B.Arch., and Paper-III for B.Planning. 

Mode of ExamJEE (Main)-2020 will be conducted in the following modes:

a) B.E. /B. Tech. in “Computer Based Test (CBT)” mode only.

b) B. Arch: Mathematics- Part I and Aptitude Test-Part II in “Computer Based Test (CBT)” mode only and Drawing Test –Part III in offline mode on drawing sheet of A4 size.

c) B.Planning : Mathematics- Part I , Aptitude Test-Part II and Planning Based Questions Part III in “Computer Based Test (CBT)” mode only 

The students advised choosing the subjects wisely, as the subjects are more clear according to the courses.