We are happy to announce that our new paper was accepted to the ACM Advances in Financial Technologies. The paper is titled “Privacy-Preserving Transactions With Verifiable Local Differential Privacy” and is by Danielle Movsowitz, Yacov Manevich (from IBM Research), and Eran Toch.

The ePrint full version of the paper can be found here.

And the Github repository code for the library is on ZKAT-VDP.

What is the paper about? In the world of blockchain networks like Monero or Zcash, privacy is paramount. These networks offer complete transaction anonymity using clever cryptographic methods like commitments and encryption. However, this high level of privacy has a downside – it makes it challenging to collect statistical data, which is crucial for financial markets and research conducted by central banks and other institutions.

To perform data analysis while guaranteeing user privacy, we’ve developed a modular scheme integrating verifiable local differential privacy techniques into the existing privacy-preserving transaction system. One of the key challenges we faced was ensuring that the differential privacy noise added to the data remains unbiased and maintains integrity.

The benefits of this coexistence are significant. Researchers can now access anonymized data for broader studies, enabling them to conduct valuable economic and sociological research without compromising user privacy, which is crucial for running full-scale financial systems over Blockchain.