Untangling the Blockchain Blockchain is just one of several distributed ledger technologies competing to shape a decentralized internet future.

In social species, consensus decision making is often a necessary survival tool, allowing groups to find food, shelter and safety from predators more efficiently than if they were working alone. Honeybees, for example, can often reach optimal consensus decisions even under dire circumstances, such as when they must immediately relocate to a new hive. If they take too long, they risk starvation, but too hasty a selection risks failure due to the environment or predators. Nature has imbued honeybees with an approach that combines both independent analysis and interdependent decision making while minimizing group disruption: scout teams. Honeybees send out scouts composed of a minority of hive members to quickly canvass as wide a terrain as possible. Each scout is not well informed about all the choices but judges a single site on several characteristics and conveys the site’s viability to its scout peers by a “waggle dance” that varies in length and enthusiasm. The success of these waggle dances usually causes the best site to acquire a quorum of scouts (at least 15 to 20 bees), which then relay the migration path to the rest of the swarm. One Cornell-led study indicates that the honeybee quorum approach results in close to the optimal trade-off between speed and accuracy.1

But consensus building is more difficult in higher social species, such as humans, who have much more heterogeneity in their makeup and motivations, as well as the potential for malicious actors. In the classic Byzantine generals’ problem, multiple divisions of an attacking army encircle a city they intend to capture.2 However, all the divisions must communicate via messenger and coordinate their attack in order to succeed. If all generals and messengers are trustworthy, the plan should unfold as intended. But how many subversive generals and messengers can the plan handle and still allow for success? In this case, a small quorum alone would not be sufficient. According to one solution, at least two-thirds of the participants would need to communicate correctly to ensure the right consensus decision.3

Different consensus decision-making algorithms are one of several factors that differentiate distributed ledger technologies (DLTs), such as blockchain, and the cryptocurrencies they power. In this article, we explore some of the underpinnings of blockchains and other DLTs as they compete to replace legacy corporate technologies and establish a foothold in a decentralized technology future.

Types of Blockchains and DLTs

A blockchain is simply a public distributed ledger that stores a historical record of append-only transactions, similar to a distributed transaction log or audit trail. These transactions are timestamped and grouped in blocks that are linked together and updated with new transactions at regular intervals based on distributed consensus agreement, and secured via cryptography (for more on the origin of blockchain and DLTs, see Bitcoin, Blockchain and Beyond).

Various versions of blockchain exist, with many overlapping features, as well as notable differences, such as their consensus algorithms. Distributed computing decision making requires more robustness than the honeybee quorum approach to achieve a reliable network state and prevent malicious or unreliable participants from destroying or hijacking the network (in computing terms, this resistance is referred to as Byzantine fault tolerance, or BFT). The blockchains underlying the cryptocurrencies Bitcoin and Ethereum rely on proof of work (PoW), a probabilistic consensus algorithm that mitigates the potential for Byzantine failures. It requires a simple majority of the computing power on its network to be run by honest and reliable participants; otherwise it could be subject to an attack vector known as a 51 percent attack.

Theoretically, if most of the Bitcoin mining power were to collude, the integrity of recent transactions on the blockchain could be at stake. But Bitcoin’s PoW algorithm has a difficulty factor designed to ensure that blocks take roughly ten minutes to mine, on average. If the price of Bitcoin goes up and more miners devote resources to trying to monopolize block mining, the difficulty factor of the PoW will rise accordingly.

PoW Flaw: Extraordinary Energy Usage

The enormous jump in Bitcoin’s price since January 2017 has led to a nearly fivefold increase in the amount of energy used in mining, comparable to the total energy consumption of a small nation. But because of difficulty factor adjustments, the environmental impact also results in an exceedingly high threshold for potentially breaching the 51 percent rule limits. Effectively, the increasing size and impact of the network help to secure it further.

Rising energy consumption could taper off, however. The number of Bitcoins awarded for mining is scheduled to halve every four years as they converge toward the maximum allocation of 21 million coins, which could reduce miners’ incentives to allocate extra hashing power to mining over time (all else equal). But in the interim, these energy and transactional performance concerns have motivated the parties governing Ethereum to investigate alternative decision algorithms, such as proof of stake (PoS), in which block creation is assigned randomly based on holdings instead of mining. In theory, PoS should operate much faster than PoW, depending on the variant, but it is not yet fully tested. Still other cryptocurrencies use an ever-growing array of alternative consensus mechanisms, such as practical BFT (used by the Hyperledger Fabric platform) and federated Byzantine agreement (versions of which have been used by the Ripple and Stellar blockchains). These achieve lower latency by utilizing voting and trust-based models, including quorum-style validation, but lack full decentralization.

Blockchain is not the only implementation available for distributed ledgers. Other technologies include directed acyclic graphs (DAGs) such as the tangle and hashgraph, which attempt to improve the scalability and performance of blockchain designs (although they are newer and not thoroughly tested). The tangle aims to be a scalable, fast network designed for microtransactions in an internet-of-things (IoT) world where multiple network-enabled devices and appliances are connected in a home or office setting and can both exchange information and transact with one another. Hashgraph is a patented solution intended for private use cases that claims extraordinarily high transactional output.

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Last year a team of Massachusetts Institute of Technology researchers4 uncovered security vulnerabilities in the tangle’s novel approach to cryptographic design (which have since been patched). This was a stark reminder of one important point about distributed technologies (and perhaps all technology): None of them can be guaranteed to be 100 percent unhackable. Even if all the participants are reliable, consensus decision making can falter in the face of unforeseen circumstances (in one of Cornell’s honeybee experiments, scout bees resorted to head-butting to break a deadlock between two identical new hive options5). But the use of established cryptography, decentralization and a time-tested consensus algorithm can raise overall network security above that of a centralized solution, although with the possible drawback of lesser efficiency.

Back to the Future

Bitcoin’s nine-year track record is by far the most successful use case for distributed ledger technologies, but potential applications for DLTs exist beyond cryptocurrency, in banking, real estate and other industries. Permissioned blockchain solutions driven by market leaders — such as Vanguard Group in its index data blockchain endeavor with technology provider Symbiont — will likely be the initial testing ground for private enterprises as DLTs compete against embedded corporate technology like distributed databases, shared intranet documents and REST API communication. The level of development is at similar early stages for other distributed computing technologies across the internet.

“At the moment, we’re at ground zero, working on the foundation,” says Vineet Choudhary, a technology consultant at D’Etat Consulting Group in Austin, Texas. Distributed file storage, databases, file transfer and domain name system (DNS) server solutions are among the many services being developed by companies like Blockstack and Bluzelle seeking to capitalize on the decentralization movement. Low-level layer work is also being created for applications in areas such as identity and governance. “Luckily, we have the internet as we know it today, and a lot of solutions and parallels can be borrowed,” Choudhary says.

The internet was conceived as a fully decentralized and distributed network, but traffic ended up being handled by service providers at centralized access points. By returning to that original vision and using newer technologies such as Wi-fi or Bluetooth to wirelessly connect users and handle network loads, communities could stay online in the event of a natural disaster or local internet shutdowns, as Brooklyn’s Red Hook community was able to do during Hurricane Sandy in 2012.6 But such “mesh network” technologies have been slow to develop as any real threat to the existing internet.

The world of IoT, with its focus on microtransactions and distributed device networking, likewise holds the promise of allowing individuals to take back ownership of their digital identities, reduce various security risks from centralized architecture and even create new monetization opportunities in ways not previously imagined. “For example, I can have a charging station on my curb that is powered by the solar grid on my roof, and cars can come to it and recharge,” explains Choudhary. “I could have a self-driving vehicle — which works for me on a decentralized Uber-like service — pick up customers and refuel itself at other people’s charging stations. Or I don’t have to take the burden of ownership of anything and simply use these services on this decentralized network.”

A fully decentralized internet has one main drawback, however. “In today’s world, central authority is often a good thing,” Choudhary notes. “Twitter, Facebook, Google and other tech giants often work with the government to shut down bad actors. When or if everything is decentralized, control will be lost.”

 

ENDNOTES

1. Thomas D. Seeley, P. Kirk Visscher and Kevin M. Passino. “Group Decision Making in Honey Bee Swarms.” American Scientist 94, no. 3 (2006): 220-29.

2. Leslie Lamport, Robert Shostak and Marshall Pease. “The Byzantine Generals Problem.” ACM Transactions on Programming Languages and Systems 4, no. 3 (1982): 382-401.

3. Pesech Feldman and Silvio Micali. “An Optimal Probabilistic Protocol for Synchronous Byzantine Agreement.” SIAM Journal on Computing 26, no. 4 (1997): 873-933.

4. Ethan Heilman, Neha Narula, Thaddeus Dryja and Madars Virza. IOTA Vulnerability Report: Cryptanalysis of the Curl Hash Function Enabling Practical Signature Forgery Attacks on the IOTA Cryptocurrency (2017).

5. Thomas D. Seeley, P. Kirk Visscher, Thomas Schlegel, Patrick M. Hogan, Nigel R. Franks and James A. R. Marshall. “Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms.” Science 335, no. 6064 (2012): 108-11.

6. Noam Cohen. “Red Hook’s Cutting-Edge Wireless Network.” The New York Times (August 22, 2014).

 

 

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