We present Aggios, a scalable and privacy preserving proxy voting system designed for frequent and large-scale elections such as Decentralized Autonomous Organizations (DAO), when storing votes on the bulletin board is expensive. To this end, Aggios introduces ‘aggregators’: entities to which voters delegate their votes, and who then post their batched proofs on the public ledger. Aggios achieves strong integrity guarantees: only authorized voters can vote, votes are counted correctly, voters are assured their vote is counted.

Abstract Data protection compliance is critical from a requirements engineering (RE) perspective, both from a software development lifecycle (SDLC) perspective and regulatory compliance. Not including these requirements from the early phases of the SDLC can prove costly and challenging afterward. The general data protection regulation (GDPR) from the European Union (EU) sets a list of requirements that organizations working within its scope should satisfy. However, these requirements are complex to work with, as legal prose tends to be vague and imprecise, and not all requirements have received the same attention from researchers. This study aims to identify the research published in RE for helping compliance with regulatory data protection requirements. We gathered and analyzed 90 articles from 2016 to 2022 through a systematic mapping study. We analyzed key trends in the sample, such as year of publication, publication venue, type of research, interdisciplinarity in the author’s background, GDPR focus of compliance element, and type of proposal. Our main findings show ongoing interest, mostly published in conferences, in achieving overall compliance with the GDPR and consent as the most popular topics. Other topics, such as cookies or children’s data, did not receive significant attention. Research over the whole RE process has been done. 20 (22%) of the papers have authors affiliated with non-computer science; however, most research seems not interdisciplinary. We finally discuss gaps in the literature, possible future areas of research, and the importance of interdisciplinary research for regulatory data protection requirements in RE.

We consider the problem of cumulative reward maximization in multi-armed bandits. We address the security concerns that occur when data and computations are outsourced to an honest-but-curious cloud i.e., that executes tasks dutifully, but tries to gain as much information as possible. We consider situations where data used in bandit algorithms is sensitive and has to be protected e.g., commercial or personal data. We rely on cryptographic schemes and propose [Formula: see text], a secure multi-party protocol based on the UCB algorithm. We prove that [Formula: see text] computes the same cumulative reward as UCB while satisfying desirable security properties. In particular, cloud nodes cannot learn the cumulative reward or the sum of rewards for more than one arm. Moreover, by analyzing messages exchanged among cloud nodes, an external observer cannot learn the cumulative reward or the sum of rewards produced by some arm. We show that the overhead due to cryptographic primitives is linear in the size of the input. Our implementation confirms the linear-time behavior and the practical feasibility of our protocol, on both synthetic and real-world data.

This exploratory paper intends to drive preliminary insights on the different mental models accountants and blockchain developers have on the implementation of blockchain for accounting. Based on the question of whether blockchain applications for accounting could be revolutionary, this paper employs a ground theory methodology based on semi-structured interviews and concept analysis to highlight the different approaches to transparency and trust between the selected groups, the challenges of blockchain and the potential effects of this technology in accounting. Although deeper studies are needed, the conclusions highlight the socio-technical nature of accounting; the relevance and changes of the concepts of trust and transparency when marrying both disciplines; and the real relevance of this technology for the processes of auditing and accounting.

We consider the problem of cumulative reward maximization in multi-armed bandits. We address the security concerns that occur when data and computations are outsourced to an honest-but-curious cloud i.e., that executes tasks dutifully, but tries to gain as much information as possible. We consider situations where data used in bandit algorithms is sensitive and has to be protected e.g., commercial or personal data. We rely on cryptographic schemes and propose UCB-DS, a distributed and secure protocol based on the UCB algorithm. We prove that UCB-DS computes the same cumulative reward as UCB while satisfying desirable security properties. In particular, cloud nodes cannot learn the cumulative reward or the sum of rewards for more than one arm. Moreover, by analyzing messages exchanged among cloud nodes, an external observer cannot learn the cumulative reward or the sum of rewards produced by some arm. We show that the overhead due to cryptographic primitives is linear in the size of the input. Our implementation confirms the linear-time behavior and the practical feasibility of our protocol, on both synthetic and real-world data.