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Breaking the Throughput Limit of Nakamoto Consensus

Breaking the throughput limit of nakamoto consensus

Breaking the Throughput Limit of Nakamoto Consensus

Breaking the throughput limit of nakamoto consensus

As shown in the two graphs at left, our compact block is slightly larger than Bitcoin’s compact block. The compact block is always propagated immediately to their neighbors.

For the newly proposed transactions, if some of them are missing in your transaction pool, they can be propagated after you send out the compact block. Those are parallel process and don’t affect each other, those transactions are verified and propagated and to the next neighbor.

You may ask:

What if a miner refuses to provide the complete version of proposed transactions?

This will not affect block propagation because blocks are propagated regardless of whether there are fresh transactions in their transaction proposal zone. The other miners will still proceed with mining because there are enough proposed transactions to confirm.

What if miners incorporate these proposed-without-broadcast transactions in their later blocks to gain a de facto selfish mining advantage?

In NC, the advantage in slow block propagation is always useful in finding the next block. For miners, they can only tell the block header and transfer the block really slowly after they find a block, during this process you are the only one who can mine the block.

However, In NC-Max, it can only be used to slow down propagation n block afterward, but only if that block is found by the attacker. Because this propagated without broadcast transactions, only the selfish miners know those transactions, and only the selfish miner can use it as an advantage. However, it can not be used in the next block because there needs to be a gap.

A miner can only mine transactions that are proposed between 2 or 5 blocks before that, you can not mine a transaction proposed in the previous block, but only if that block is found by an attacker.

As the above picture shows, in NC when the selfish miner finds the block, it can immediately start mine the h+1 block, however, honest miners can only start mining after they receive the full block — that’s the selfish miner’s advantage during the block propagation period.

In NC-Max, when a selfish miner finds a block h, an honest miner can immediately start mining block h+1.

If the selfish miner wants to utilize this proposal without broadcasting transactions, it has to find blocks n block afterward, only then can the selfish miner utilize this advantage. However, this happens less often. You can not make sure that after 7 blocks there will be a block mined by me, it’s very hard to predict that.

2. Dynamic Block Interval and Block Reward to Best Utilize Bandwidth

NC-Max uses a different difficulty adjustment mechanism which targets a fixed orphan rate (counted as uncles in the last difficulty adjustment period):

If the orphan rate in the last difficulty period is below the target orphan rate, the difficulty will go down, the block interval will go down and the throughput will go up.

In other words, very few orphans mean that the network can synchronize transactions faster, which means we can increase the throughput without harming the decentralization.

Otherwise the difficulty increase, the block interval will increase and the throughput will decrease. A higher orphan rate means that the network can not process these many transactions in the difficulty adjustment period.

The block reward is proportional to the inverse of the expected block interval, so the expected total reward per difficulty adjustment period is fixed. For example: if we have 10 minutes per block, each block has 12.5 Bitcoin; if we had 5 minutes per block, each block can have 6.125 Bitcoin. So the issue rate of the currency is always fixed.

3. Considering All Blocks in Difficulty Adjustment to Defend against Selfish Mining

In NC-Max, the difficulty adjustment mechanism counts all blocks, including uncles when estimating total mining power.

In NC, without the selfish mining attack, the attacker finds 3 blocks in 10, the honest miner finds 7. With the attack, the attacker finds 3 blocks in 7, the honest miners find 4, 3 honest blocks become orphaned, as the main chain grows slower, the difficulty lowers, the attacker can find more blocks with the same mining power.

In NC-Max, the difficulty will stay the same as the difficulty adjustment mechanism counts all blocks. The attacker can not find more blocks with the same mining power, therefore selfish mining is no longer profitable!

In conclusion, NC-Max makes use of orphaned blocks. We want to reduce the number of orphans by two-step transaction confirmation. After the orphan is reduced, we use the remaining ones as an indicator of bandwidth utilization to adjust the throughput, and given that the uncle information is embedded in the blockchain, we can use them to make selfish mining unprofitable (yeah!)

1. Why we love Nakamoto Consensus (NC)

2. Forget about the TPS Competition

3. A detailed look into the design of NC-Max, a variant of Nakamoto Consensus with higher throughput.

P.S. NC-Max is a temporary name for Nervos consensus protocol — if you have any good idea on how it can be named please tell us in comments 🙂

Published at Thu, 07 Mar 2019 03:11:17 +0000

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Cybersecurity Firm Reports All Fortune 500 Companies Exposed on the Dark Web

Cybersecurity Firm Reports All Fortune 500 Companies Exposed on the Dark Web

Every Fortune 500 company has some level of exposure on the dark web, with technology and telecommunications firms ranking at the top of the list, according to a report published by Denver-base OWL Cybersecurity at the end of May, 2017.

The dark web, unlike the surface web — the internet most people know of and use every day — can’t be indexed using traditional search engines, such as Google or Bing. The cybersecurity company has built a database updated with “10 to 15 million pages per day, from more than 24,000 domains on the Tor network alone, as well as other darknet networks.” With the darknet content indexed and searchable in 47 different languages, OWL claims their dark web database is the “most comprehensive one of its kind in the world.”

In the study, the OWL picked each and every company from the 2017 Fortune 500 list and assessed them with an overall darknet footprint. OWL uses a specific algorithm for the purpose, rating postings on the dark web based on their potential for criminal use.

“To compile our Darknet Index, we ran each member of the 2017 Fortune 500 through the OWL Vision database. We focused on specific darknets for matches on each company’s website and email domains and then further adjusted the results based on computations of ‘hackishness,’” the report reads.

When valuable information is either stolen or hacked, the data is often offered for sale on the dark web, OWL stated. On dark web marketplaces and forums, criminals exchange illegal products and data — mostly sourced from hacks and breaches — for cryptocurrencies, such as bitcoin. Therefore, the cybersecurity company measured the exposure of the Fortune 500 firms by analyzing their presence on the darknet.

According to the researchers, in some instances, “private data for sale may have come from a breach at a Fortune 500 company, but it may not be identified as such.” OWL explained, for example, that multiple instances of credit card information up for sale on the dark web can come from various sources, including banks or retailers; however, information on the source of the compromised data is not always available or provided.

OWL ranked the Fortune 500 companies by their Darknet Index score — calculated by the cybersecurity firm’s algorithm — and also included the firms’ rankings on the Fortune 500 lists. Ranked by DARKINT (darknet intelligence), technology companies lead the list, with Amazon holding the top spot, but with telecommunications firms right alongside it.

The cybersecurity firm pointed out some key takeaways from their analysis. The researchers emphasized that all Fortune 500 companies have a presence on the dark web since “every single company in the Fortune 500 had a positive Darknet Index score.” OWL explained Amazon’s top ranking with the fact that the firm has a “massive internet presence and possesses a significant amount of customer data.”

The researchers were surprised by the comparatively positive rankings of financial firms, which are frequent targets of cybercriminals. OWL indicated that the financial industry’s significant investment in cybersecurity measures in recent years was the reason for the success. Other sectors in which the firms invested “heavily” in cybersecurity also had lower Darknet Index ratings, the researchers added.

OWL expects that by publishing such statistics in their report, they can help companies improve their cybersecurity. The cybersecurity firm enables companies with compromised data to monitor the stolen or hacked information on the dark web.

“Today, in an age where data loss is virtually inevitable, it is critical to look at the darknet as a key part of a complete cybersecurity program, enabling organizations to swiftly detect security gaps and mitigate damage prior to the misuse of data.”

The post Cybersecurity Firm Reports All Fortune 500 Companies Exposed on the Dark Web appeared first on Bitcoin Magazine.