Project type: Explore Project

Accountability Privacy Preserving Computation via Blockchain


The project 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).

Value Creation

Using this kind of auditable continuous secure computation can help fight discrimination and catch unethical and fraudulent behaviour. Computations that advance these goals include aggregate statistics on salary information  to help identify and eliminate wage gaps (e.g. as seen in the case of the Boston wage gap study [4]), statistics on bids in an auction or bets on a gambling site to determine whether those bids or bets are fraudulent, and many others. Organizations would not be able to carry out such computations without the use of privacy-preserving technologies due to privacy regulations; so, secure computation is necessary here. To be useful, these secure computations crucially require authenticity and consistency of the inputs. Organizations, which will not necessarily be driven by altruism, will have several incentives to participate in these computations. First, by using secure computation to detect fraud, the participants can guard against financial loss. Second, when participants are public organizations, honest participation (which anyone can verify) will generate positive publicity.



Bernardo David

Associate Professor

IT University of Copenhagen
Department of Computer Science


Sophia Yakoubov

Assistant Professor

Aarhus University
Department of Computer Science


James Chiang

PhD Student

IT University of Copenhagen
Department of Computer Science
Technical University of Denmark Department of Computer Science
(co-supervised with Alberto Lluch Lafuente)

Anne Dorte Rafn Spangsberg

Cryptographic Engineer

The Alexandra Institute