Accountable Privacy Preserving Computation via Blockchain

We will investigate how to combine secure multiparty computation and blockchain techniques to obtain more efficient privacy-preserving computation with accountability. Privacy-preserving computation with accountability
allows computation on private data (without compromising data privacy),
while obtaining an audit trail that allows third parties to verify that the computation succeeded or to identify bad actors who tried to cheat.
Applications include data analysis (e.g. in the context of discrimination detection and bench marking) and fraud detection (e.g. in the financial and insurance industries).

The main results of this project were the following: 1. A systematisation of knowledge (SoK) describing how different state-of-the-art privacy enhancing technologies (PETs) can be used in the traditional and decentralised financial sector, including applications to anti money laundering (AML) and Know Your Client (KYC) as well as applications to financial markets where privacy is a concern; 2. a new cryptographic primitive for auditing financial transactions in both traditional and decentralised (e.g. blockchain-based) financial systems in such a way that an auditor learns nothing but whether a given sequence of transactions has a accumulated a risk rating higher than a certain threshold or exceeded a given threshold of fund movements.