Alexander Hicks

Zhangqi Duan, Nigel Fernandez, Alexander Hicks et al.

Open-ended coding tasks, which ask students to construct programs according to certain specifications, are common in computer science education. Student modeling can be challenging since their open-ended nature means that student code can be diverse. Traditional knowledge tracing (KT) models that only analyze response correctness may not fully capture nuances in student knowledge from student code. In this paper, we introduce Test case-Informed Knowledge Tracing for Open-ended Coding (TIKTOC), a framework to simultaneously analyze and predict both open-ended student code and whether the code passes each test case. We augment the existing CodeWorkout dataset with the test cases used for a subset of the open-ended coding questions, and propose a multi-task learning KT method to simultaneously analyze and predict 1) whether a student's code submission passes each test case and 2) the student's open-ended code, using a large language model as the backbone. We quantitatively show that these methods outperform existing KT methods for coding that only use the overall score a code submission receives. We also qualitatively demonstrate how test case information, combined with open-ended code, helps us gain fine-grained insights into student knowledge.

Sarah Azouvi, Alexander Hicks

Cryptocurrencies have garnered much attention in recent years, both from the academic community and industry. One interesting aspect of cryptocurrencies is their explicit consideration of incentives at the protocol level. Understanding how to incorporate this into the models used to design cryptocurrencies has motivated a large body of work, yet many open problems still exist and current systems rarely deal with incentive related problems well. This issue arises due to the gap between Cryptography and Distributed Systems security, which deals with traditional security problems that ignore the explicit consideration of incentives, and Game Theory, which deals best with situations involving incentives. With this work, we aim to offer a systematization of the work that relates to this problem, considering papers that blend Game Theory with Cryptography or Distributed systems and discussing how they can be related. This gives an overview of the available tools, and we look at their (potential) use in practice, in the context of existing blockchain based systems that have been proposed or implemented.

Sarah Azouvi, Haaroon Yousaf, Alexander Hicks

Privacy was one of the key points mentioned in Nakamoto's Bitcoin whitepaper, and one of the selling points of Bitcoin in its early stages. In hindsight, however, de-anonymising Bitcoin users turned out to be more feasible than expected. Since then, privacy focused cryptocurrencies such as Zcash and Monero have surfaced. Both of these examples cannot be described as fully successful in their aims, as recent research has shown. Incentives are integral to the security of cryptocurrencies, so it is interesting to investigate whether they could also be aligned with privacy goals. A lack of privacy often results from low user counts, resulting in low anonymity sets. Could users be incentivised to use the privacy preserving implementations of the systems they use? Not only is Zcash much less used than Bitcoin (which it forked from), but most Zcash transactions are simply transparent transactions, rather than the (at least intended to be) privacy-preserving shielded transactions. This paper and poster briefly discusses how incentives could be incorporated into systems like cryptocurrencies with the aim of achieving privacy goals. We take Zcash as example, but the ideas discussed could apply to other privacy-focused cryptocurrencies. This work was presented as a poster at OPERANDI 2018, the poster can be found within this short document.

Alexander Hicks, Vasilios Mavroudis, Mustafa Al-Bassam et al.

We propose VAMS, a system that enables transparency for audits of access to data requests without compromising the privacy of parties in the system. VAMS supports audits on an aggregate level and an individual level, by relying on three mechanisms. A tamper-evident log provides integrity for the log entries that are audited. A tagging scheme allows users to query log entries that relate to them, without allowing others to do so. MultiBallot, a novel extension of the ThreeBallot voting scheme, is used to generate a synthetic dataset that can be used to publicly verify published statistics with a low expected privacy loss. We evaluate two implementations of VAMS, and show that both the log and the ability to verify published statistics are practical for realistic use cases such as access to healthcare records and law enforcement access to communications records.