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One of the common misconceptions I encounter when explaining threat modeling to people is the is of operating system scale. This is one of those cases where size really does matter.

When threat modeling, there is a desire to do as little work as possible. By that I mean that you shouldn’t model the same thing multiple times. Model it once put it in a box and move on. It’s furniture.

We do this to allow us to focus on the stuff we’re developing and not third-party or open source bits.

When it comes to operating systems, however, I don’t have just one border to deal with as I would with say a vendor provided driver. The thing we casually refer to as an operating system is actually a many layered beast and should be treated as such. When we do, the issue of OS scale disappears in a puff of abstraction smoke.

So, what is this so far unexplained scale?

Let’s rewind a bit to the original computers. They were slow, small (computationally and with respect to storage) and, in the grand scheme of things, pretty simple. There was no operating system. The computer executed a single program. The program was responsible for all operational aspects of its existence.

As computers became more sophisticated, libraries were created to provide standardized components, allowing developers to focus on the core application and not the plumbing. Two of these libraries stand out: the mass storage and communications libraries. We would eventually refer to these as the file system and network.

When computers began expanding their scope and user base, the need for a mechanism to handle first sequential, then later multiple jobs led to the development of a scheduling, queueing and general task management suite.

By the time Unix was introduced, this task manager was surrounded by access management, program development tools, general utilities and games. Because, well, games.

For users of these systems, the OS became shorthand for “stuff we didn’t need to write.”

The odd thing is that, on the periphery, there existed a class of systems too small or too specialized to use of even require this on-stop-shopping OS. These were the embedded systems. For decades, these purpose-built computers ran one program. They included everything from thermostats to digital thermometers. (And yes, those Casio watches with the calculator built in.)

Over time, processors got a lot more powerful and a lot smaller. The combination of which made it possible to run those previously resource hungry desktop class operating systems in a little tiny box.

But what happens when you want to optimize for power and space? You strip the operating systems down to their base elements and only use the ones you need.

This is where our OS sizing comes from.

I like to broadly divide operating systems into four classes:

  • bare metal
  • static library
  • RTOS
  • desktop / server

Each of these presents unique issues when threat modeling. Let’s look at each in turn.

Bare Metal

Probably the easiest OS level to threat model is bare metal. Since there’s nothing from third-party sources, Development teams should be able to easily investigate and explain how potential threats are managed.

Static Library

I consider this the most difficult level. Typically, the OS vendor provides sources which development builds into their system. Questions of OS library modification, testing specific to target / tool chain combination, and threat model (OS) arise. The boundaries can become really muddy. One nice thing is that the only OS elements are the ones explicitly included. Additionally, you can typically exclude aspects of the libraries you don’t use. Doing so, however, breaks the de-risking boundary as the OS vendor probably didn’t test your pared down version.

RTOS

An RTOS tends to be an easier level than a desktop / server one. This is because the OS has been stripped down and tuned for performance and space. As such, bits which would otherwise be laying about for an attacker to leverage are out of play. This OS type may present issues in modeling as unique behaviors may present themselves.

Desktop / Server

This is the convention center of operating systems. Anything and everything that anyone has ever used or asked for may be, and probably is, available. This is generally a bad thing. On the upside, this level tends to provide sophisticated access control mechanisms. On the downside, meshing said mechanisms with other peoples’ systems isn’t always straightforward. In the area of configuration, as this is provided by the OS vendor, it’s pretty safe to assume that any configuration-driven custom version is tested by the vendor.

OS and Threat Modeling

When threat modeling, I take the approach of treating the OS as a collection of services. Doing so, the issue of OS level goes away. I can visually decompose the system into logical data flows into process, file system and network services; rather than a generic OS object. It also lets me put OS-provided drivers on the periphery, more closely modeling the physicality of the system.

It’s important to note that this approach requires that I create multiple threat model diagrams representing various levels of data abstraction. Generally speaking, the OS is only present at the lowest level. As we move up the abstraction tree, the OS goes away and only the data flow between the entities and resources which the OS was intermediating will be present.

Let’s consider an application communicating via a custom protocol. At the lowest level, the network manages TCP/UDP traffic. We need to ensure that these are handled properly as the transit the OS network service. At the next level we have the management of the custom protocol itself. In order to model this properly, we need for the network service to not be involved in the discussions. Finally, at the software level, we consider how the payload is managed (let’s presume that it’s a command protocol).

Nowhere in the above example does the OS level have any impact on how the system would be modeled. By decomposing the OS into services and treating layers uniformly, we gain the ability to treat any OS like furniture. It’s there, but once you’ve established that it behaves properly, you move on.

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There have been numerous times when a new technology has led to a major shift in how we thought about how computers and software should be built. We are about to see one of those shifts. At least that’s what I’ve come to believe.

Let’s pop into the Wayback and set our sights on the early ’80s. At that time computers had one processor. Hardware-based floating point were the domain of mainframes and minicomputers. Communications between computers existed only for the well-heeled. Security meant keeping your computer locked up.

Life was pretty simple. If you wanted something done, you did it yourself. When software was shared it was done via the US Postal Service on 9-track tape.

Fast forward to the early ’90s. Desktop computers were fairly common. Uniprocessors still ruled. Hardware floating point was now readily available. The internet had just been introduced. Gopher was slowly to be displaced by the combination of FTP and web search engines. Security issues were a thing that happened, but was, on the whole a black art practiced by a small number of individuals and required skills that you needed to develop yourself.

It was around this time that I was casting about for a thesis topic for my Master’s in Electrical and Computer Engineering. I took on the topic of virus-resistant computer architectures (AARDVARK). Did I mention that it was 1992? Just researching the state of the art in computer viruses was a huge task. No Google, Amazon or ACM online article search. As to the other side of the equation, the how and why of hacking, well, I’ll leave that for another time.

By the time I was done, I’d proposed a computer architecture with separate instruction and data spaces where the application’s binary was encrypted and the key loaded in a separate boot sequence was stored in a secure enclave, accessible only to the binary segment loader. Programs were validated at runtime. I conjectured that such a computer would be ideal for secure use and could be built within the 18 months.

Everyone thought it was a great design and the school even worked with me to apply for a patent. The US Patent Office at that time didn’t get it. After five years we abandoned the effort. I was disappointed, but didn’t lose sleep over it.

Fast forward to 2012 when Apple released the iOS 6 security guidelines. Imagine my amusement when I see echos of AARDVARK. It’s all there: signed binaries, secure enclave, load validation. Good on them for doing it right.

Let’s step back and consider the situation. Computers are really small. They have integrated hardware floating point units, multi-processors and now, with the advent of this generation of iPhone, hardware-based security. The internet has gone global. Google indexes everything, Open source is a thing. So, we’re good?

Not so much. The Apple iPhones are an oasis in a vast desert of security badness. Yes, IPv6 has security goodness available, but IPv4 still rules. Secure programming practices are all but non-existent. Scan and contain is the IT mantra. Threat modeling is an exercise for the academic.

This brings to last year. Microsoft announced Azure Sphere. Application processor, dual-MCU, networking processor, security processor. All firewalled. All in the same package. The provided OS was a secured version of Linux. Each device is registered so only the manufacturer can deploy software, push updates and collect telemetry via the Azure cloud.

There must be a catch. Well, as you know, there’s no such thing as a free burrito.

The first device created to the Azure Sphere specification is the Mediatek MT3620. And no, you can’t use it for your next laptop. The target is IoT. But, there’s a lot of horsepower in there. And there’s a lot of security and communications architecture that developers won’t have to build themselves.

Microsoft is touting this a the first generation. Since they started with Linux and ARM, why wouldn’t you want to get something with more power for systems that have security at their core. If Microsoft approached this as Apple has the iPhone, iPad, AppleTV and Apple Watch; why shouldn’t we expect consumer computers that aren’t insecure.

But will I be able to use them for software development? That’s a tricky question.

When I envisioned AARDVARK, my answer was no. That architecture was designed for end-user systems like banks and the military. You can debug a Sphere device from within Visual Studio, so, maybe it’s doable. You’d need to address the issue of a non-isomorphic ownership model.

Are users willing to bind their device to a single entity? Before you say no, consider how much we’re already put in the hands of the Googles and Facebooks of the world. Like it or not, those are platforms. As are all the gaming systems.

Regardless, I believe that we will end up with consumer compute devices based on this architecture. Until then we’ll just have to watch to see whether the IoT sector gets it and by extension the big boys.

Either way, the future is Sphere.

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When I was an undergraduate, I heard a story about a DEC PDP 11/70 at a nearby school that had a strange hardware mod. A toggle switch had been added by someone and wired into the backplane apparently. The switch had two settings, “magic” and “more magic.” The identity of the individual or individuals having made the mod was lost. For as long as anyone could remember, the switch had been in the “magic” position. One day, some brave soul decided to find out what happened when the “more magic” setting was selected. Upon flipping the toggle, the machine crashed. It thereafter resisted any attempts to get it to run. After a bit, they gave up, flipped the toggle back to “magic“, power cycled the machine and hoped. The machine returned to its previous state of operational happiness. One could say that they’d been trying to achieve too much of a good thing.

We might read this and come away with the idea that, well, they just should have gotten on with their work and not worried about the plumbing. That’s certainly the majority view, from what I’ve seen. But why was the switch there in the first place? If it only worked in one position, shouldn’t they have just wired things without the switch?

Let’s consider the temporal aspect, no one remembered who, when, or why, let alone what. It may well have been the case that once “more magic” actually worked. Who can say. That whole documentation thing.

When I work with project teams and individual developers, I have a habit of saying “no magic.” It comes from having heard this story. I’ll say this when working with individuals who’s code I’m reviewing, teams who’s architecture I’m reviewing, or leads and architects while facilitating the creation of threat models. I don’t care whether the magic manifests as constants (magic numbers) from who-knows-where or logic that’s more convoluted than a Gordian Knot. Basically, if the reason that something is being used exists without the benefit of understanding, it shouldn’t be there. I don’t care who put it there or how smart they were. Someday someone is going to come along and try to change things and it will all go south. Enough of these in a code review and it’s likely to get a single summary review comment of “no.”

How does this relate to security? Do you know what the auto-completion for “never implement your” is? I’ll let you try that on you own, just to verify. Never implement your own crypto[graphic functions]. Why? Where should I start? The math is torturous. The implementation is damn hard to do right. Did you know that you can break poorly implement crypto via timing analysis? Even if you don’t roll your own crypto, are you using some open source library or the one from the operating system? Do you know when to use which? Are you storing your keys properly?

Magic, all of it.

Some people believe that security can be achieved by obscuring things. These also tend to be the same people who’ve never used a decompiler. You’d be amazed what can be achieved with “a lot of tape and a little patience.”

If your goal is to have software and systems that are secure, you can’t have magic. Ever.

So, when I see company with a core philosophy of “move fast, break things,” I think well aren’t they going to have more attack surface than a two pound chunk of activated carbon. Not amazingly, they did and we are worse off because of it.

You can’t secure software-based systems unless you understand how the pieces play together. You can’t understand how the pieces play together until you understand how each piece behaves. You can’t understand how a piece behaves if it’s got magic floating around in it. It’s also important to not just glom onto a technique or technology because it’s handy or trendy. As Brian Kernighan and P.J. Plauger said, “it is dangerous to believe that blind application of any particular technique will lead to good programs[2].”

While you’re out there moving fast and tossing things over the wall, keep in mind that someone else, moving equally fast, is stitching them together with other bits. The result of which will also be tossed over another wall. And while it is true that some combination of these bits product interesting and quite useful results, what is the totality of their impact? At what point are we simply trusting that the pieces we’re using are not only correct and appropriate to our use, but don’t have any unintended consequences when combined in the way we have done.

You need to know that every part does the thing you intend it to do. That it does it correctly. And that, it does nothing you don’t intend. Otherwise, you’re going to have problems.

I’ll close with another story. In the dim days, before people could use the Internet (big I), there were a number of networks. These were eventually interconnected hence the name interconnected networks or Internet for short. Anyway, back in the day (early ’80s), universities were attaching to the Internet backbone, which was in and of itself pretty normal. What was not normal was when someone accidentally mounted a chunk of the Andrews File System (AFS) onto an Internet node. It ended up mounting the entirety of AFS on the Internet. This had the unexpected side effect of making a vast number of students previously unprotected emails publicly available to anyone with Internet access. Mostly that meant other university students. AFS wasn’t actually designed to be connected to anything else at that time. Bit of a scandal.

Unintended consequences.


  1. Image credit: Magic Book By Colgreyis ©Creative Commons Attribution 3.0 License.
  2. Kernighan and Plauger, Software Tools, 1976, page 2 paragraph 4

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When creating a class, it’s important to have a motivating example. In my experience, people learn best when they can see an immediate application to their own work. In the area of cybersecurity, this can be difficult. Examples in this space tend to be either too esoteric (return-oriented programming) or too divorced from the domain (credit card theft).

I’ve just finished up the creation of a two hour software security fundamentals class for management and developers. This is intended to provide a framework for integrating security into the software development process. Build it in vs. bolt it on. As I was putting the class together, the motivating example was just out of reach.

The push-back that must be overcome is that there already exists a process for dealing with security issues. It’s an extension to the standard quality assurance process. This process merely needs to be extended to include security-related testing, right?

Let’s look at that assertion for a moment.

How exactly does quality assurance work? Well, it’s based, by and large, on the flawed hypothesis model. Starting with the documentation, test cases are created to verify the assertions made therein. From there, scenarios are imagined. These are likewise verified. If issues (bugs) are discovered, generalizations are attempted. Any found point to larger problems in the code.

Sounds good, what’s the problem?

Consider the internet joke:

QA engineer walks into a bar. They order a beer, then order 0 beers, then order 999999999 beers, then orders a lizard, then orders -1 beers, then orders a eawlirensadk.

A customer walks into the bar and asks where the bathroom is. The bar bursts into flames, killing everyone.

That’s pretty much the problem with the flawed hypothesis model. You only verify the things you think of. If you’re only looking at how the bar serves beer, you’ll never catch issues involving other aspects of the system (here, bathroom location).

It’s a bit extreme as a motivating example, but everyone can relate to it, which is, of course, the point.

From there, the concept of flaws vs. bugs can emerge. QA finds bugs. On a good day, these may point to flaws. So, what’s the difference. For the purposes of discussion, flaws are design defects and bugs are implementation (code) defects. By its very nature, QA does not test design, only implementation.

At this point, management asks the question, isn’t this how it’s always been? Generally speaking, yes. Long gone are the days when people used program design language (PDL) to reason about the soundness of their software. At that time, security wasn’t much of a focus.

Enter threat modeling. By its very nature, threat modeling allows us to reason on the design. Why? Because it focuses not on the documentation, but rather the data flows and through extension the work flows of the system. Because we abstract ourselves from the implementation, we can reason about the system in ways that point us directly to security flaws.

To relate the impact to the real world, one has only to look at the cost to Samsung of not catching a design flaw in the Note 7 prior to release (US$17B). IBM estimates that relative to catching an issue a the design stage that the cost is 6.5 time higher in the implementation stage, 15 time higher during testing, and 100 times higher after release.

I’m in no way advocating the elimination of QA testing. You need both. As well as the processes we do in between, such as code reviews and static / dynamic analysis. But again, discovering issues in these stages of development is going to be more expensive. Defense-in-depth will always give you a better result. This is true not only in security, but the development process itself.

As I was finishing up my software security fundamentals class, the news broke regarding a high-profile technology firm that exposed the private data (images) of millions of individuals via their developer APIs. This is probably a case of failing to threat model their system. This isn’t the first time that this particular company has failed miserably in the area of security. It points out, in ways which greatly assist my efforts to get management on-board, that the failed hypothesis model is no substitute for critical analysis of the design itself.

As a system grows in complexity, it is critical to abstract out the minutiae and let the data flows point toward possible issues in the design. Threat modeling is one technique, but not the only one, that makes that possible.

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As someone in the technology sector, on a fairly constant basis I get asked the grown-up equivalent of “what do you want to be when you grow up.” This is, of course, “where do you see yourself in N years.”

Now, most of the time, this is a question with all the gravity of “nice day, isn’t it?” Sometime, however, the inquiry is sincere. And my answer is provided with the same weight as the question.

And, for reference, my answer hasn’t changed all that much since I was about seven. Happily, the way that I answer has become a bit more sophisticated. My end game position is that of CTO (Chief Technology Officer).

Many of my contemporaries have gone the route of management. This is cool with me. You shouldn’t be doing engineering and science if you don’t have it in you. By in you, I intend the sense given by The Oracle in The Matrix when she told Neo that you know that you’re the one when you feel it “balls to bones.” Seriously. There are far easier ways to make a decent living than the constant demands and uncertainty that comes along with the endeavor of technological advancement. Hell, forget advancement, just using technology is a hard slog.

For me, working with technology and constantly expanding the reach of my understanding within that sphere is one of my core drives.

So like anything else I’ve ever set as a goal, I researched this thing I’ve set my sights on.

Let’s unwrap what I understand today.

It’s relative new

As C-suite positions go, the CTO is really young. Only the CISO (Chief Information Security Officer) position is newer. As you’d imagine it’s not like there weren’t technology companies before CTO roamed the Earth. Prior to recognizing that the technology landscape was changing so quickly and on such a continual basis that a board-level position focusing exclusively on the implications of such change, technology was the domain of either the CIO (Chief Information Officer) or CEO (Chief Executive Officer).

There was a realization that technology falls into two broad categories: present and future. You can think of these as tactical (product development) and strategic (futures research). Investopedia says that a CTO “examines the short and long term needs of an organization, and utilizes capital to make investments designed to help the organization reach its objectives … [the CTO] is the highest technology executive position within a company and leads the technology or engineering department.”

This division of labor is not unlike the was that Computer Science became an independent discipline. It too is dual-rooted. There were schools where the computer (singular) was managed by the Math department and those managed by the Electrical Engineering department. You can tell the difference in the focus in curriculum. It will be either theoretical (math) or applied (engineering) in nature.

It’s not only one

The position of CTO is in no way one-size-fits-all. Presently, it’s possible to identify four distinct sub-species of CTO. This diversity reflects the nature of the companies and how technology fits into their culture and mission.

We can identify these four by where the fall on the spectrum described by amount of business change and percentage of products and services based on information.

 

CTO quadrants

As can be seen, these are four very different animals. This is why you would expect the CTO from a relatively stable business in the manufacturing sector like GE (big thinker) to be very different from one at a business experiencing near constant change and highly-dependent on information in its products like Facebook (visionary). Neither of those would look anything like the stable business, high-dependency Apple (external-facing) or high change, low-dependency AT&T (information manager).

The Infrastructure Manager

CTO quadrant - infrastructure manager

 

Typically seen in companies with low dependency on information-related technologies, but with business models experiencing large amount of change (technology change impacting how the business is run), the Infrastructure Manager CTO reports to the CIO and is responsible for addressing how to build out and leverage technology to reduce cost and encourage technology adoption across business units in order to gain efficiencies.

The Big Thinker

 

CTO quadrants - big thinker

The Big Thinker CTO is the response to never-ending growth of things utilizing information technology. We see this type in companies with stable business models and a relatively low dependence on information as a part of their products. We see their focus on strategic initiatives such as:

  • Advanced technology
  • Competitive analysis
  • Technology assessment
  • Prototyping
  • Planning
  • Setting architectural standards
  • These CTO answer to the CEO and peer the CIO. Here we have a division of the IT and engineering departments. They act as change agents, typically having a relatively small elite staff. They are influencers rather than controllers.

The Visionary and Operations Manager

 

CTO quadrant - visionary

In companies in the throws of business change (increased technology complexity) and highly dependent upon information in their products and services, the CTO will be the Visionary and Operations Manager type. Answering to the CEO, this is the prime mover of the company. Their responsibilities are all encompassing. They drive business strategies and exploit new technologies and then implement those same technologies throughout the business and product groups. We see the CIO reporting to the CTO in this view of the world.

The External-facing Technologist

 

CTO quadrant - external technologist

Information-driven companies with stable business models will tend to have the External-facing Technologist CTO. As with the Big Thinker type, this CTO peers the CIO with both answering to the CEO. Here the focus in on the identifying new technologies, exploiting them, and evangelizing them both in and outside the organization.

Areas of Impact

If we visualize the areas of impact for the four type, we can see the natural focus areas for each.

infrastructure managerbig thinkervisionaryexternal technologist

Observations

Greg Brockman, Stripe’s CTO, said that other CTO’s “viewed themselves as the facilitators of the technology organization. Sometimes this was about connecting senior engineers. Sometimes it was mentoring. … I realized the most important thing to do was to empower our engineers to make big changes and improvements.”

“It’s not a simple job to understand all the technology out there,” says Unisys Corp’s global CTO Fred Dillman. “Today the pace of change is so much faster, and businesses are becoming more and more dependent on technology. So the CTO is being asked to be the real expert in technology and understanding what technologies will affect the business in the future and help determine when and where to invest.”

My fit

So, where do I see myself in all this? Tricky question.

Honestly, it varies. As the Version Control Systems Architect at Metrowerks, I was evangelizing source code control. At The Altamira Group, I rocked the visionary thing. Most of my CTO-esque activities have fallen into the Big Thinker bucket. Researching futures and educating engineers and management is where I spend the bulk of my time.

Roger Smith noted that “[t]he significant role of technology in strategic business decisions has created the need for executives who understand technology and recognize profitable applications to products, services and processes. many companies have addressed this need through the appointment of a chief technology officer (CTO) whose responsibilities include:

  • monitoring new technologies and assessing their potential to become new products and services
  • overseeing the selection of research projects to ensure that they have the potential to add value to the company
  • providing reliable technical assessments of potential mergers and acquisitions
  • explaining company products and future plans to the trade media
  • participating in government, academic and industry groups where there are opportunities to promote the company’s reputation and to capture valuable data

Integrating these technology-based activities into the corporate strategy requires that the CTO nurture effective relationships with key people throughout the company. These include the CEO, members of the executive committee, chief scientists, research laboratory directors, and marketing leaders.”

Regardless of the specific needs of the organization, I’ll continue to strive to provide the best information in a timely fashion to those who need it.

References

 

 

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Sometimes you can spend years trying to find a book that you can recommend to someone who’s asked you a question. My latest read, The Software Craftsman: Professionalism, Pragmatism, Pride is one such book. A recent volume in the Robert C. Martin book series, this volume by Sandro Mancuso is not what it appears to be. And that, is a good thing.

When you look at the other books in the Martin series (Working Effectively with Legacy Code, Agile Estimating and Planning, Clean Code, The Clean Coder, Clean Architecture, …) you see topics decomposed and methodologies expressed by which the title’s subject is achieved. That’s not what you get with The Software Craftsman. In my case, that was a very fortunate turn of events.

This is not to say that the journey of the software craftsman is not discussed. It is and in a reasonable amount of detail. But an equal amount of time is given to the ecosystem within which the craftsman practices. These parts of the book are not for the consumption of the craftsman or aspirant, but for the owners of the firms who employ (or should employ) them.

The book does well in describing the trials and tribulations of a member of the craft; from the point where they realized that they aspired to more than the dichotomy of coder / architect; to the creation of the volume itself. It lays bare this false dichotomy within the broader context of the entire point of software development. That being to produce value to the customer and income to the creator. Within that context, there is the easy path of whatever works and the hard path of building a thing that no only does what it is supposed to, but does it in a way which is both high quality and highly maintainable.

At it’s core, this is book about philosophy. In a landscape of Google and go; and compile it, link it, ship it, debug it; this is a thoughtful volume. It makes the point that I’ve never seen in print, that the individual software developer is responsible for their own career development. Not their manager, not their company, but they themselves are responsible. Heady stuff this.

As to the remainder of the book’s material, it’s more a wake up call to upper management. There you’ll find discussion of recruiting, hiring, retaining, shaping change and showing ROI. I know of very few who could look at this volume and come away unmoved.

It might be the separation of authority and responsibility, the hire for what we needed yesterday, the CYA so we get our bonus, or the factory worker mentality encouraged by so many firms today. If you can read this book and not get something out of it, you’re part of the problem.

Truly quality software is designed, built, and tested by passionate individuals working together toward the creation of something which will well serve the customer. Everything else is just code. Any 10 year-old can be taught to write code. I know, I’ve done it. Do you want your life’s critical systems to be build by 10 year-olds? Of course not, that’s a ridiculous question. How about people who are just doing it because they make a better than average day’s wage?

I hope you’re intrigued. At the very least, I hope you’ll reflect on your own views of the responsibilities of a software developer. At fewer than 250 pages, you can read this book in one or two sittings, but reading the book is only the starting point.

 

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There’s been a lot of churn lately over the price of Bitcoin. There’s also been much talk about uses (commercial and otherwise) for the blockchain technology that underlies it. I’ve taken an interest, as of late, in the subject and I found a promising source of information in the Michael J Casey, Paul Vigna book The Truth Machine.

If you’re looking for a book on how to write blockchain software. This book isn’t going to be of much interest to you. If you have interest in the history of the technology, its applications and societal impacts, you will find it to be a solid read.

Bitcoin and  Ethereum, and the like are discussed with enough depth to provide non-developers a good sense of use cases. This is after all the point of a technology. Otherwise you’ve got yourself shelfware. All the major players are discussed running the gamut from crypto-anarchists to Wall Street bankers. I find the dynamic of these two extremes battling over this technology fascinating.

One definitely comes away with the sense that the technology is still very much a work in progress. Personally, I find it unfortunate that the public sees it as another get-rich-quick methodology. This loops back to the volatility of the Coin markets.

Another, and arguably more important, takeaway is that the systems currently in place do not scale (or at least have yet to be proven to scale). That Bitcoin can process six transactions per second in a world where Visa processed ten thousand is quite telling.

The underlying problem of who’s in charge comes to mind. Without some form of human governance, poor programming will result in bad actors taking advantage of the system. Nowhere does the book address the issue of longevity. The thing about paper (or animal hide for that matter) is that it has a permanence that has proven itself outside of our advances in storage technology. If we did move to a blockchain-based system of ownership tracking, what happens when another, better one comes along? What will the impact of quantum computing be in a world where work is proportional to CPU expended? What happens when people decide that it’s easier to steal the resources used to mine the coin?

I only have a few issues with the book.

First, for a book on a complex technological subject, I expect extra fact checking. I noticed two rookie mistakes in this department. ASIC is defined as ‘Application-Specific Integrated Chips’ whereas it should be ‘Application-Specific Integrated Circuit.’ It also doesn’t require quotes. In the realm of techno-history, the authors attributed Public-Key Cryptography to Whitfield Diffie and Martin Hellman. This is of course incorrect. That distinction belongs to James H Ellis (at least until the NSA owns up to when they started using it). Although his work was once classified, it have been publicly recognized for some time now.

Second, the book wanders off the path of techno-history and into the realm of  conspiracy theory and political opinion when they introduce ‘speculation’ of hacking in the MH370 incident and spend the bulk of the last chapter on the latter.

On the whole, I found the book to be a very good and current survey of the landscape of blockchain technology and its history.

 

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