The DLT team of ING Bank is performing tests with a privacy feature called ‘bulletproofs’ developed by the Blockstream startup

ING Bank Trials ‘Bulletproofs’ Blockchain Solutions for Turning Banks’ BTC Transactions Invisible

Previous tests attempted by ING
Real-life application
As reported by Coindesk, the privacy ensuring technology ‘bulletproofs’ was created by the teams of the Blockstream startup and scientists at Stanford University and University College London.

These ‘bulletproofs’ make it impossible for third parties to see amounts of funds which are sent in BTC transactions. Banks wish to provide their customers with more privacy regarding blockchains and keep their private data in a safer environment.

Previous tests attempted by ING
Last year, ING Bank tested zero-knowledge proofs (ZKPs) for the same purpose. The bank has tried range proofs (when a concealed number is hidden in a certain range). Another option the bank trialed was zero-knowledge set membership, which allows for alphanumeric data validation only within a specified set.

However, ZKPs consume a lot of computation power and thus make blockchain systems work slower. The bank believes that bulletproofs will prove to be much more effective and will be easier to apply.

The head of ING’s DLT initiative, Mariana Gomez de la Villa, says that the bulletproofs are about tenfold faster than other range proof solutions when it comes to a single range proof.

Presently, ING Bank is seeking where it can apply its new technology. If used in real life, the use cases will have to comply with the European Union’s General Data Protection Regulation (GDPR).

One of the possible examples of the technology’s use is that ZK membership can help a person prove his/her citizenship of any EU country. And the person will not have to disclose any other personal identification data.

The creators of the ‘bulletproofs’ technology seem excited and encouraged, admitting they did not expect that things would turn this fortunate for their invention and that it would be applied to solve real-life problems.

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By Yuri Molchan