“The most powerful techniques are open consensus methods, that protect from changes, while also allowing for incentivising all sorts of processes, ranging from the consensus itself to standardisation processes”

or participants. A blockchain is just one specific type of DLT. Not all DLTs use a chain of blocks, for example, and not all distributed databases are DLTs – the trust boundaries are different. A DLT delegates access to functions and

data over multiple parties through secure cryptographic principles. This platform works in tandem with Porsche’s traditional centralised servers, but the novel network becomes part of the backend with better trust and security, and gives network users a new level of access between ‘read’ and ‘write’ permissions and more. DLTs are most famously known for underpinning the digital currency Bitcoin. In 2018, Bitcoin mining represented roughly 0.6% of global energy demand – equivalent to Argentina’s consumption. Even though Bitcoin mining consumes lots of energy, a DLT network controlled algorithmically does not necessarily have to consume more energy per node. ‘First of all, I think blockchain is

outdated. Blockchain became a buzzword, we even used it on our company name,’ said Robert Küfner, advisor to Advanced Blockchain, a German publicly listed company focusing on the design, development and deployment of DLTs. ‘Using more energy in order to solve a problem is simply wrong,’ said Küfner. ‘All | @scwmagazine

of this will be obsolete because code can replace the work that currently requires lots of energy. This goes back to what we are doing in Advanced Blockchain. ‘If we look at distributed ledger

technology, blockchain is just one... the majority of people and start-ups are focussing on Fintech, everything around finance and money. But there are a few industries with a higher potential that will disrupt sooner than others; those industries that involve a middle party have a problem with the decentralised movement,’ said Küfner. The automotive sector is one such area.

It can enable better oversight of odometer fraud to car buyers, such as clocking or busting miles, which is the illegal practice of rolling back odometers to make it appear that vehicles have lower distances travelled, costing millions. ‘The vehicle identification number, the time stamp and actual mileage of the vehicle will be uploaded and stored to a ledger,’ said Küfner; ‘which cannot be compromised. By doing so, you have a digital twin logbook of the car.’

Opening up to more reproducible scientific research A distributed approach to data also opens up possibilities for academia. In

August, researchers at the San Diego Supercomputer Center (SDSC), of the University of California San Diego, US, were awarded a three-year National Science Foundation grant of $818,000 to design and develop an infrastructure of open-source distributed ledger technologies to enable researchers to efficiently share information about their scientific data, while preserving the original information. The hope is that researchers will be able to efficiently access and securely verify data, according to the SDSC press release. Known as the Open Science Chain, this is not a supercomputer-related project according to Subhashini Sivagnanam, principal investigator for the grant and a scientific computing specialist with SDSC’s data-enabled scientific computing division. This infrastructure will be integrated with actual scientific datasets. The Open Science Chain aims to

increase productivity, security and reproducibility. As the datasets change over time, new information will be appended to the chain. ‘My first impressions are that it may help part of the problem – validating and verifying the data,’ said Les Hatton, emeritus professor of forensic software engineering at Kingston University, London, UK. Hatton, who is not involved in this

research, said: ‘However, nothing is said about the software which analyses that data. Full computational reproducibility depends on the whole package: data, analysis software, glue software and testing software.’ However, the large-scale challenge of

reproducing scientific data is bigger than just one technical approach, Hatton states.

October/November 2018 Scientific Computing World 27 g


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