arXiv subjects

Marwa Mouallem

Publications and source records attributed to Marwa Mouallem.

4 recordsLinked to original sources

arXiv2026

Crypto x AI, AI x Crypto: A Survey

The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportunities and challenges are, and what open questions deserve attention. This survey paper asks what AI can do for blockchain-based technologies (broadly construed as "crypto") (crypto x AI), and vice versa (AI x crypto). We systematize existing work, summarize key takeaways, highlight open research questions, and offer a perspective on pervasive industry misconceptions, concluding that AI and crypto are still in the very early stages of meaningful integration.

cs.CR↗

arXiv2026

Resilient Alerting Protocols for Blockchains

Smart contracts are stateful programs deployed on blockchains; they secure over a trillion dollars in transaction value per year. High-stakes smart contracts often rely on timely alerts about external events, but prior work has not analyzed their resilience to an attacker suppressing alerts via bribery. We formalize this challenge in a cryptoeconomic setting as the \emph{alerting problem}, giving rise to a game between a bribing adversary and~$n$ rational participants, who pay a penalty if they are caught deviating from the protocol. We establish a quadratic, i.e.,~$O(n^2)$, upper bound, whereas a straightforward alerting protocol only achieves~$O(n)$ bribery cost. We present a \emph{simultaneous game} that asymptotically achieves the quadratic upper bound and thus asymptotically-optimal bribery resistance. We then present two protocols that implement our simultaneous game: The first leverages a strong network synchrony assumption. The second relaxes this strong assumption and instead takes advantage of trusted hardware and blockchain proof-of-publication to establish a timed commitment scheme. These two protocols are constant-time but incur a linear storage overhead on the blockchain. We analyze a third, \emph{sequential alerting} protocol that optimistically incurs no on-chain storage overhead, at the expense of~$O(n)$ worst-case execution time. All three protocols achieve asymptotically-optimal bribery costs, but with different resource and performance tradeoffs. Together, they illuminate a rich design space for practical solutions to the alerting problem.

cs.CR↗

arXiv2024

Asynchronous Authentication

A myriad of authentication mechanisms embody a continuous evolution from verbal passwords in ancient times to contemporary multi-factor authentication. Nevertheless, digital asset heists and numerous identity theft cases illustrate the urgent need to revisit the fundamentals of user authentication. We abstract away credential details and formalize the general, common case of asynchronous authentication, with unbounded message propagation time. Our model, which might be of independent interest, allows for eventual message delivery, while bounding execution time to maintain cryptographic guarantees. Given credentials' fault probabilities (e.g., loss or leak), we seek mechanisms with the highest success probability. We show that every mechanism is dominated by some Boolean mechanism -- defined by a monotonic Boolean function on presented credentials. We present an algorithm for finding approximately optimal mechanisms. Previous work analyzed Boolean mechanisms specifically, but used brute force, which quickly becomes prohibitively complex. We leverage the problem structure to reduce complexity by orders of magnitude. The algorithm is readily applicable to practical settings. For example, we revisit the common approach in cryptocurrency wallets that use a handful of high-quality credentials. We show that adding low-quality credentials improves security by orders of magnitude.

cs.CR↗

arXiv2022

Executing Instructions in Situated Collaborative Interactions

We study a collaborative scenario where a user not only instructs a system to complete tasks, but also acts alongside it. This allows the user to adapt to the system abilities by changing their language or deciding to simply accomplish some tasks themselves, and requires the system to effectively recover from errors as the user strategically assigns it new goals. We build a game environment to study this scenario, and learn to map user instructions to system actions. We introduce a learning approach focused on recovery from cascading errors between instructions, and modeling methods to explicitly reason about instructions with multiple goals. We evaluate with a new evaluation protocol using recorded interactions and online games with human users, and observe how users adapt to the system abilities.

cs.CL↗