Interview: Steve Grobman, McAfee CTO shares his views on some burning security questions

Post quantum cryptography is fast becoming a popular topic in the cyber …. Returning to the topic of post-quantum encryption, Grobman says it is a …

Post-quantum encryption algorithms

The US National Institute of Standards and Technology (Nist) is currently evaluating post-quantum encryption algorithms submitted by cryptography experts and plans to publish a selection of the best in the next couple of years that will be used as industry standards for the post-quantum era.

“But part of the problem, in my mind, is that the current timeline is working in a granularity of years, and it will literally be a few years before potential post-quantum algorithms are identified, and we are acting serially that once the algorithms are identified, we will start looking at moving those algorithms into protocols, and eventually we will look at moving those protocols into products.”

This slow pace of progress is a cause for concern, according to Grobman, particularly in the light of the timeline for other big initiatives such as internet protocol version 6 (IPV6).

“The IPV6 specification was finalised 20 years ago, and is still not widely implemented and used,” he says. “I think it is important to have more pragmatists involved in the standards process because running this process just out of research and academia can result in having elegant solutions, but missing some of the practical challenges of deploying in real-world environments.”

Grobman believes it is important that the security industry starts thinking about the implementation of post-quantum cryptographic algorithms before we know what the algorithms are going to be.

“If we were building a new airplane and another company was designing the engine, we wouldn’t wait for them to finish the engine design before we start working on the rest of the plane. Instead, we would start building the rest of the plane and bring the two pieces of technology together as soon as they are ready, so we would end up with the finished airplane much faster.”

Read more about quantum computing

In the case of post quantum cryptography, Grobman says the industry should be looking at some of the top contenders in the submitted algorithms and identify what paradigm changes would be needed in things like transport layer security (TLS), digital certificate management and other forms of encryption key management.

Although Grobman agrees it “wouldn’t hurt” for organisations to increase the length of their encryption keys in the short term, even fairly long keys will be vulnerable to attack once practical quantum cryptanalysis is achieved, so increasing the key length may provide a “false sense of security”.

Given the doubtful benefit of increasing key lengths, he recommends instead that organisations plan for an “aggressive” re-tooling of the protocols ahead of post quantum algorithms being available.

Reiterating that this process is likely to take several years, Grobman also emphasises the importance of finding algorithms that are not only quantum resistant, but also resistant to classic key cracking methods.

“The nuance of finding secure algorithms, as we have seen time and time again, is difficult,” he says. “MD5, RC4, triple DES, one after the other vulnerabilities were found, but luckily things like AES [advanced encryption standard] has so far stood the test of time and is quantum resistant [because it is a symmetric key algorithm].

“It is the public key [asymmetric] cryptography algorithms that are the ones that are of most concern, like the RSA algorithm and elliptic curve cryptography, which are the ones that are going to be the biggest challenge in the post quantum era.”

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Artificial intelligence: Parking a car with only 12 neurons

A naturally grown brain works quite differently than an ordinary computer program. It does not use code consisting of clear logical instructions, it is a …

IMAGE

IMAGE: This is the neural net with different layers of interconnected neurons. view more

Credit: TU Wien

A naturally grown brain works quite differently than an ordinary computer program. It does not use code consisting of clear logical instructions, it is a network of cells that communicate with each other. Simulating such networks on a computer can help to solve problems which are difficult to break down into logical operations.

At TU Wien (Vienna), in collaboration with researchers at Massachusetts Institute of Technology (MIT), a new approach for programming such neural networks has now been developed, which models the time evolution of the nerve signals in a completely different way. It was inspired by a particularly simple and well-researched creature, the roundworm C. elegans. Neural circuits from its nervous system were simulated on the computer, and then the model was adapted with machine learning algorithms. This way, it was possible to solve remarkable tasks with an extremely low number of simulated nerve cells – for example parking a car. Even though the worm-inspired network only consists of 12 neurons, it can be trained to steer a rover robot to a given spot. Ramin Hasani from the Institute of Computer Engineering at TU Wien has now presented his work at the TEDx conference in Vienna on October 20.

It can be shown that these novel neural networks are extremely versatile. Another advantage is that their internal dynamics can be understood – in contrast to standard artificial neural networks, which are often regarded as a useful but inscrutable “black box”.

Signals in branched networks

“Neural networks have to be trained” says Ramin Hasani. “You provide a specific input and adjust the connections between the neurons so that the desired output is delivered.”

The input, for example, can be a photograph, and the output can be the name of the person in the picture. “Time usually does no play an important role in this process,” says Radu Grosu from the Institute of Computer Engineering of TU Wien. For most neural networks, all the input is delivered at once, immediately resulting in a certain output. But in nature things are very different.

Speech recognition, for example, is always time-dependent, as are simultaneous translations or sequences of movements reacting to a changing environment. “Such tasks can be handled better using what we call RNN, or recurrent neural networks”, says Ramin Hasani. “This is an architecture that can capture sequences, because it makes neurons remember what happened previously.”

Hasani and his colleagues propose a novel RNN-architecture based on a biophysical neuron and synapse model that allows time-varying dynamics. “In a standard RNN-model, there is a constant link between neuron one and neuron two, defining how strongly the activity of neuron one influences the activity of neuron two”, says Ramin Hasani. “In our novel RNN architecture, this link is a nonlinear function of time.”

The Worm Brain that can Park a Car

Allowing cell activities and links between cells to vary over time opens up completely new possibilities. Ramin Hasani, Mathias Lechner and their coworkers showed theoretically that their architecture can, in principle, approximate arbitrary dynamics. To demonstrate the versatility of the new approach, they developed and trained a small neural network: “We re-purposed a neural circuit from the nervous system of the nematode C. elegans. It is responsible for generating a simple reflexive behavior – the touch-withdrawal,” says Mathias Lechner, who is now working at the Institute of Science and Technology (IST) Austria. “This neural network was simulated and trained to control real-life applications.”

The success is remarkable: the small, simple network with only 12 neurons can (after appropriate training) solve challenging tasks. For instance, it was trained to manoeuvre a vehicle into a parking space along a pre-defined path. “The output of the neural network, which in nature would control the movement of nematode worms, is used in our case to steer and accelerate a vehicle”, says Hasani. “We theoretically and experimentally demonstrated that our novel neural networks can solve complex tasks in real-life and in simulated physical environments.”

The new approach has another important advantage: it provides a better insight into the inner workings of the neural network. Previous neural networks, which often consisted of many thousands of nodes, have been so complex that only the final results could be analysed. Obtaining a deeper understanding of what is going on inside was hardly possible. The smaller but extremely powerful network of the Vienna team is easier to analyse, and so scientists can at least partially understand, which nerve cells cause which effects. “This is a great advantage which encourages us to further research their properties”, says Hasani.

Of course, this does not mean that cars will be parked by artificial worms in the future, but it shows that artificial intelligence with a more brain-like architecture can be far more powerful than previously thought.

###

Contact:

Dott. Mag. Ramin Hasani

Institute for Computer Engineering

TU Wien

Treitlstraße 3, 1040 Wien

T: +43-1-58801-18228

ramin.hasani@tuwien.ac.at

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Elon Musk posts bizarre bitcoin tweet about cryptocurrency scam epidemic

Elon Musk has inadvertently drawn attention to a cryptocurrency scam epidemic that has engulfed Twitter in recent months, when his account was …

Elon Musk has inadvertently drawn attention to a cryptocurrency scam epidemic that has engulfed Twitter in recent months, when his account was locked after tweeting about bitcoin.

For almost a year, the serially successful entrepreneur has been the target of cryptocurrency scammers on Twitter, who impersonate his profile picture and account name in order to trick his followers into sending them bitcoin, ethereum and other cryptocurrency.

Other high-profile figures to be targeted include US President Donald Trump, singer Katy Perry and cyber security pioneer John McAfee.

ShapeCreated with Sketch.Bitcoin’s volatile history in pictures

Show all 8
leftCreated with Sketch.rightCreated with Sketch.

1/8 Satoshi Nakamoto creates the first bitcoin block in 2009

On 3 January, 2009, the genesis block of bitcoin appeared. It came less than a year after the pseudonymous creator Satoshi Nakamoto detailed the cryptocurrency in a paper titled ‘Bitcoin: A peer-to-Peer Electronic Cash System’
Reuters

2/8 Bitcoin is used as a currency for the first time

On 22 May, 2010, the first ever real-world bitcoin transaction took place. Lazlo Hanyecz bought two pizzas for 10,000 bitcoins – the equivalent of $90 million at today’s prices
Lazlo Hanyecz

3/8 Silk Road opens for business

Bitcoin soon gained notoriety for its use on the dark web. The Silk Road marketplace, established in 2011, was the first of hundreds of sites to offer illegal drugs and services in exchange for bitcoin

4/8 The first bitcoin ATM appears

On 29 October, 2013, the first ever bitcoin ATM was installed in a coffee shop in Vancouver, Canada. The machine allowed people to exchange bitcoins for cash
REUTERS/Dimitris Michalakis

5/8 The fall of MtGox

The world’s biggest bitcoin exchange, MtGox, filed for bankruptcy in February 2014 after losing almost 750,000 of its customers bitcoins. At the time, this was around 7 per cent of all bitcoins and the market inevitably crashed
Getty Images

6/8 Would the real Satoshi Nakamoto please stand up

In 2015, Australian police raided the home of Craig Wright after the entrepreneur claimed he was Satoshi Nakamoto. He later rescinded the claim
Getty Images

7/8 Bitcoin’s big split

On 1 August, 2017, an unresolvable dispute within the bitcoin community saw the network split. The fork of bitcoin’s underlying blockchain technology spawned a new cryptocurrency: Bitcoin cash
REUTERS

8/8 Bitcoin’s price sky rockets

Towards the end of 2017, the price of bitcoin surged to almost $20,000. This represented a 1,300 per cent increase from its price at the start of the year
Reuters

1/8 Satoshi Nakamoto creates the first bitcoin block in 2009

On 3 January, 2009, the genesis block of bitcoin appeared. It came less than a year after the pseudonymous creator Satoshi Nakamoto detailed the cryptocurrency in a paper titled ‘Bitcoin: A peer-to-Peer Electronic Cash System’
Reuters

2/8 Bitcoin is used as a currency for the first time

On 22 May, 2010, the first ever real-world bitcoin transaction took place. Lazlo Hanyecz bought two pizzas for 10,000 bitcoins – the equivalent of $90 million at today’s prices
Lazlo Hanyecz

3/8 Silk Road opens for business

Bitcoin soon gained notoriety for its use on the dark web. The Silk Road marketplace, established in 2011, was the first of hundreds of sites to offer illegal drugs and services in exchange for bitcoin

4/8 The first bitcoin ATM appears

On 29 October, 2013, the first ever bitcoin ATM was installed in a coffee shop in Vancouver, Canada. The machine allowed people to exchange bitcoins for cash
REUTERS/Dimitris Michalakis

5/8 The fall of MtGox

The world’s biggest bitcoin exchange, MtGox, filed for bankruptcy in February 2014 after losing almost 750,000 of its customers bitcoins. At the time, this was around 7 per cent of all bitcoins and the market inevitably crashed
Getty Images

6/8 Would the real Satoshi Nakamoto please stand up

In 2015, Australian police raided the home of Craig Wright after the entrepreneur claimed he was Satoshi Nakamoto. He later rescinded the claim
Getty Images

7/8 Bitcoin’s big split

On 1 August, 2017, an unresolvable dispute within the bitcoin community saw the network split. The fork of bitcoin’s underlying blockchain technology spawned a new cryptocurrency: Bitcoin cash
REUTERS

8/8 Bitcoin’s price sky rockets

Towards the end of 2017, the price of bitcoin surged to almost $20,000. This represented a 1,300 per cent increase from its price at the start of the year
Reuters

Mr Musk has previously spoken out about the scam epidemic, tweeting in July: “I want to know who is running the Etherium scambots! Mad skillz.”

This prompted a response from ethereum co-founder Vitalik Buterin, who pleaded with Twitter CEO Jack Dorsey to “help us”.

Research published in August by cyber security firm Duo Security revealed a network of 15,000 bot accounts that had been set up to dupe people into falling for cryptocurrency scams.

Twitter has attempted to crack down on the bots, suspending more than 1 million fake and suspicious accounts each day in recent months.

This is presumably why Twitter temporarily locked Mr Musk’s account on Monday night, after he posted a picture of an Anime character in a bitcoin-branded dress, together with the words: “Wanna buy some bitcoin?”

Mr Musk’s tweet elicited around 12,000 likes, 4,000 retweets and more than 700 replies, many of which from prominent people within the cryptocurrency community.

Changpeng Zhao, the CEO of the popular exchange Binance, tweeted: “Lol, it’s spreading. I will buy a Tesla if you accept crypto…”

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Mr Musk’s prolific use of Twitter has drawn both positive and negative attention to him and the companies he overseas.

In August, he tweeted plans to take Tesla private, which caused the firm’s stock price to briefly spike. This led to fraud charges being brought against him by the US Securities and Exchange Commission (SEC), which were eventually settled in September.

Part of the settlement involves Tesla’s board putting in place “additional controls and procedures to oversee Musk’s communications.”

But this requirement only comes into effect 90 days after signing it, giving Mr Musk free reign on Twitter for at least another couple of months.

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Three signs it’s time to invest in data science

Big data is no longer big news. Everyone is using it. Or at least, making an attempt to incorporate some data-driven practices into their marketing setup …

Big data is no longer big news. Everyone is using it. Or at least, making an attempt to incorporate some data-driven practices into their marketing setup. The problem? Data has become massively complex and encompassing. Contextualising insights, obtained from a multitude of sources, requires an expert eye.

But hiring a data scientist can seem a steep investment. So, should you consider taking the plunge? Yes, if you can relate to any of the following problems:

You cannot measure and attribute your marketing ROI

Business value comes as a direct result of quantifying the expected outcomes from your marketing campaigns. However, when dealing with scattered and siloed marketing data, it’s easy to misinterpret what your data is trying to tell you.

Only 21% of marketers use analytics to measure marketing ROI for all marketing engagement. We can assume that most marketers choose to evaluate a selected few activities, but again – there are a lot of blind spots left unattended. When it comes to content marketing, 47% of B2B marketers cannot measure the exact ROI. Further, 44% of businesses struggle to estimate social media marketing ROI and quantify the campaign results.

Data science enables you to capture and even predict those elusive numbers. You can choose to analyse any number of campaigns on a granular level so that you can connect specific insights to marketing challenges and outcomes.

For instance, you can deploy algorithms to gather customers’ multiple identifiers across different channels (email address, data for cookies, phone number etc.) into a single customer profile. Such profiles could then be segmented into smaller groups and continuously updated as new information becomes available eg. the customer’s response to certain email campaigns or personalised content marketing digest. Over time, your system will be able to identify lookalike groups of prospects that are likely to respond well to certain types of advertising.

The end result is that you are no longer second-guessing your ROI. You know exactly what will impact it and what results you can expect when you apply action Y to audience Z.

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You struggle to connect with your customers at crucial points

Customer journeys are no longer as simple as – “saw a product”, “bought it”, “placed an order”. Instead, they behave as if they are exploring a maze – make some u-turns, wander off and stop all of a sudden. Your job is to guide them towards the right exit – your offer.

Google has recently identified four new micro-moments, shaping how consumers now interact with information and brands:

  • “I-want-to-know” moments – 66% of smartphone users pick up their gadgets to look up something they saw in a TV commercial.
  • “I-want-to-go” moments – 82% of smartphone users look up a local business before visiting.
  • “I-want-to-do” moments – 91% grab their smartphones for ideas when doing a task.
  • “I-want-to-buy” moments – 82% of users consult their smartphones in-store when deciding on a purchase.

Staying on top of all their intentions is nearly impossible if done manually. Data science can help you locate the current touch points with your customers, attribute them to a specific stage of the customer journey: pre-purchase, purchase and post-purchase – and help identify the missed opportunities for connection (based on available data).

For example, a lot of shoppers prefer to place orders online to minimise the risk of not finding the product they want when visiting a nearby store. So how do you bring more customers indoors? Macys.com found an interesting solution. The retailer re-targets nearby customers with local inventory ads when they are in a nearby area. Such personalised ads display a pair of shoes in the size and colour the customer searched for earlier and increases their willingness to visit a store and make a purchase that day.

Data science can help you identify such micro-moments and conduct real-time, personalised marketing experiments, based on a multitude of different touchpoints.

You want to remain competitive without hiring more people

Staff costs can be crippling for growing companies. Data science can increase your teams’ performance without multiplying the number of desks in the office. Some common marketing processes you can outsource to the algorithms are:

  • Advanced lead scoring. Your team will focus on converting the top 5% prospects, selected by the algorithm, rather than waste time on combing through a list of some 10,000 names.
  • Prioritised marketing action. Machine learning tools can help your team remain focused on what matters most, instead of wasting time on low-value chores.
  • Automatic campaign management. Pause, fine-tune and scrap marketing campaigns that yield low results.
  • Dynamic pricing. Develop models that rely on data-backed variables to adjust prices in real time instead of relying on hunches.
  • Better personalisation. Implement intimate strategies without spending time second-guessing customers’ intentions or responses to your pitch.

Your ability to capitalise on data is crucial to your business success. Analytics tools are handy as they provide you with data to crunch, albeit they cannot tell you what your next big marketing idea should be. They can only tell you when that idea is working or not. Data science, on the contrary, can help spell out the “what”, “how” and “why” of each marketing action you have planned.

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George Karapalidis is head of data science at Vertical Leap

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Bitcoin After 10 Years

However, Bitcoin’s fixed quantity path creates a different problem that inhibits its widespread use as currency. With the number of Bitcoins …

The end of this month (31 October 2018) will mark the 10th anniversary of the online posting of the now-famous white paper by “Satoshi Nakamoto” outlining the concept of “Bitcoin: A Peer-to-Peer Electronic Cash System.” This is an opportune occasion to compare what Bitcoin has achieved with what Satoshi wanted to achieve. While Bitcoin’s rise to a market valuation of over $100 billion is certainly a remarkable accomplishment of one sort, the founder had other aims.

Three problems with the status quo

In announcing the new project in February 2009 Satoshi emphasized three institutional problems with the status quo payment system that Bitcoin would address. First, inflation from central banks that issue fiat money:

The root problem with conventional currency is all the trust that’s required to make it work. The central bank must be trusted not to debase the currency, but the history of fiat currencies is full of breaches of that trust.

Second, a lack of privacy and security from commercial banks:

We have to trust them with our privacy, trust them not to let identity thieves drain our accounts.

Third, the high cost of bank-mediated payments:

Their massive overhead costs make micropayments impossible.

How well has Bitcoin addressed these three problems?

Inflation risk and purchasing power volatility

Satoshi wanted to create a currency with less risk of inflation and devaluation. It is of course true that the history of fiat currencies is full of breaches of trust in purchasing-power stability. Central banks issuing fiat money have chronically, and sometime acutely, diluted the value of their currencies by expanding them too rapidly. Bitcoin’s source code, which predetermines the quantity path of the stock of Bitcoins, does solve that problem. There can be no unexpectedly rapid expansion. This code provides a valuable object lesson in how to write a constitutional monetary rule that is fully automatic and free from discretion.

However, Bitcoin’s fixed quantity path creates a different problem that inhibits its widespread use as currency. With the number of Bitcoins unresponsive to demand shifts, all the burden of adjustment falls on the price (purchasing power). As a result the market price of Bitcoin is enormously volatile week-to-week and even day-to-day. This makes it very risky to hold or accept BTC as a payment medium for monthly bills that are denominated in anything other than BTC (e.g. in US dollars, other fiat currencies, or commodity index baskets).

Satoshi recognized that demand growth would cause secularly rising value, but said little about the problem of high-frequency volatility of value. He did not design Bitcoin to have an automatically demand-responsive supply, because he did not know how to do it without creating the need for a trusted authority:

[I]ndeed there is nobody to act as central bank or federal reserve to adjust the money supply as the population of users grows. That would have required a trusted party to determine the value, because I don’t know a way for software to know the real world value of things. If there was some clever way, or if we wanted to trust someone to actively manage the money supply to peg it to something, the rules could have been programmed for that.

What Satoshi didn’t know how to do is still not known. The desirability of a stable-valued cryptocurrency has, however, has stimulated dozens of “stablecoin” projects in recent years. There are two main types: (a) coin supply managed by an “algorithmic central bank” that automatically (given a data feed) varies quantity to stabilize purchasing power, and (b) coin supply made endogenous by pegging the coin to a relatively stable fiat currency, to gold, or to a commodity basket. A recent report on “The State of Stablecoins” has identified 57 projects, of which 23 are up and running. Tether USD, imperfectly pegged to the US dollar, is by far the largest of the live projects. Of the 57, twelve use the “algorithmic central bank” approach, the remainder being “asset-backed” either by fiat currency collateral or by cryptoassets. The problem remains unsolved of feeding a program with real-world data in a tamperproof way, or of running a currency peg without any risk to customers from dishonesty or incompetence by the party holding the reserves.

Satoshi suggested—somewhat inaccurately—that Bitcoin would behave like gold under a gold standard:

In this sense, it’s more typical of a precious metal. Instead of the supply changing to keep the value the same, the supply is predetermined and the value changes.

In fact, as I have noted before, the classical gold standard system provided a great deal of long-run elasticity to the quantity of money. A rising purchasing power of gold incentivized the owners of existing mines to dig deeper and increase their output, and encouraged prospectors to seek new sources of gold. The accumulation of increased gold flow over time pushed the purchasing power back to its nearly flat long-run trend. The gold standard thereby historically constrained the inflation rate to near zero in the long term.

F. A. Hayek’s vision of competing non-commodity private monies imagined that issuers would maintain purchasing power stability by actively managing supply. A new project called Initiative Q takes basically this approach: not a cryptocurrency governed by a program, but a private non-commodity money whose quantity is governed by a human board that pledges to stabilize its purchasing power. Full disclosure: I have been a paid consultant on this project.

Satoshi anticipated a feature of Bitcoin’s fixed supply path that has played an important role in its enormous appreciation, and in its high volatility:

As the number of users grows, the value per coin increases. It has the potential for a positive feedback loop; as users increase, the value goes up, which could attract more users to take advantage of the increasing value.

In this way attracting speculators who want an appreciating store of value (and don’t care much about short-term volatility) is at root incompatible with attracting potential currency-users who want short-term value predictability. Having attracted speculative “hodlers,” it is harder to expand the set of Bitcoin users much beyond them.

Retail use of Bitcoin remains small, from all available indicators. The largest BTC retail payment processor, Bitpay, reported in October 2017 that its merchants are receiving “$110 M+ in bitcoin payments per month,” which multiplies out to $1.32 billion per year. For comparison, VISA reported in June 2018 an annual payment volume of $11 trillion, or $11,000 billion.

Coinmap.org lists 13,365 brick-and-mortar Bitcoin acceptance points worldwide, which is of course a tiny subset of retail establishments. Checking the map for Fairfax County, VA, I find that there are only seven sellers of goods and services listed, plus another 7 Bitcoin ATMs.

Privacy

Satoshi wanted to create a payment system with greater privacy. Bitcoin does enable users to send funds outside the financial panopticon that is the regulated banking system, where “Know Your Customer” and “Anti- Money Laundering” edicts require banks to surveil customer account use and report certain kinds of activity. This escape hatch has allowed ordinary people to protect their wealth from restrictions such as exchange controls and from confiscatory taxes. For example, Bitcoin became suddenly popular in Cyprus when the government imposed controls on international bank transfers and proposed to take 10 percent of bank balances during a banking crisis in 2013.

However, the way Bitcoin’s distributed ledger system shares addresses and size information about every transaction provides less privacy than would a design sharing less information. Bitcoin is not anonymous, only pseudonymous, and the pseudonyms can be pierced. This shortcoming has inspired a number of “privacycoin” projects. The best known live projects are Monero, Dash, and Zcash (for head-to-head contrasts of these and three others see here). Two interesting up-and-coming projects, using a newer-generation blockchain technology called MimbleWimble, are Beam and Grin.

Cheaper payments

As far as making micropayments at negligible cost, the Bitcoin blockchain has turned out to be infeasible for doing so. It becomes quickly congested as it approaches the modest volume of 7 transactions per second. This technological limitation was discussed by insiders (Hal Finney, Nick Szabo) as early as 2010, but did not come to wider attention until massive congestion arose with Bitcoin’s expansion in popularity in 2017, bringing a sharp rise in fees for moving your transaction to the front of the queue. Developers are now working on “sidechains” for small payments—most famously the Lightning Network—that will settle only periodically on the main BTC blockchain. So there may be a technical workaround retaining the Bitcoin standard. The MimbleWimble projects represent another approach: because their blockchains are designed to transmit much less information among miners, they should not only provide greater privacy, but also handle many more transactions per second.

Conclusion

Bitcoin should not be regarded as the last word in private money, but should be appreciated as a remarkable technological breakthrough. Ten years after its launch, we must recognize it as the innovation that has launched financial and non-financial blockchain industries that are still in their early days. Bitcoin has established its value as an asset, and its usefulness as a medium of exchange for a certain subset of transactions. It is the main unit of account and payment medium, preferred to fiat monies, for markets in other cryptocurrencies below the top five. Whether it will achieve common use as a medium of exchange remains doubtful. The inbuilt volatility of its purchasing power makes it unlikely to displace the incumbent fiat currencies barring an inflationary explosion. Even in that case, gold seems likely to prove more popular. Whether a credible stablecoin built on Bitcoin’s shoulders, or some completely different approach, will achieve critical mass as a private money remains to be seen.

[Cross-posted from Alt-M.org]

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