Yunusa Simpa Abdulsalam

arXiv2026

upTPM: Unbounded Preprocessing for Schnorr Multi-Signatures on TPM

Schnorr-based multi-signature schemes support offline preprocessing of nonce commitments to reduce online signing to a single round. However, preprocessing is inherently bounded: each preprocessed nonce pair consumes signer-side storage, and once exhausted, an interactive commitment round is required to refill. This limitation is particularly severe for TPM~2.0 devices, where usable NVRAM is typically 6--16\,KB and connectivity is intermittent. This paper presents upTPM, a framework that achieves unbounded preprocessing with constant signer storage. Each TPM stores a single 32-byte secret seed from which an unlimited sequence of nonce commitments is deterministically derived. Commitments are published to an untrusted coordinator before use; nonce scalars never leave the TPM. We formalize three properties not provided by existing schemes: (1)~unbounded deterministic preprocessing with constant storage; (2)~asynchronous commitment refill, allowing any signer to unilaterally extend its commitment pool; and (3)~TPM-attested commitments, a hardware-backed authenticity and state-binding mechanism that strengthens resistance to host-software compromise. We prove EU-CMA security in the random oracle model under the discrete logarithm assumption and Pseudo Random Function (PRF) security, with a one-time-use invariant enforced by TPM hardware state. We extend the construction to $(t,n)$-threshold signatures and provide a detailed analysis of coordinator trust, crash recovery, and performance evaluations.

cs.CR↗

arXiv2025

Enduring Disparities in the Workplace: A Pilot Study in the AI Community

In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and employee contentment. In AI and Machine Learning (ML), such environments correlate with higher standards of responsible development. Without transparency, disparities, microaggressions and misconduct will remain unaddressed, undermining the very structural inequities responsible AI aims to mitigate. While prior work investigates workplace transparency and disparities in broad domains (e.g. science and technology, law) for specific demographic subgroups, it lacks in-depth and intersectional conclusions and a focus on the AI/ML community. To address this, we conducted a pilot survey of 1260 AI/ML professionals both in industry and academia across different axes, probing aspects such as belonging, performance, workplace Diversity, Equity and Inclusion (DEI) initiatives, accessibility, performance and compensation, microaggressions, misconduct, growth, and well-being. Results indicate enduring disparities in workplace experiences for underrepresented and/or marginalized subgroups. In particular, we highlight that accessibility remains an important challenge for a positive work environment and that disabled employees have a worse workplace experience than their non-disabled colleagues. We further surface disparities for intersectional groups and discuss how the implementation of DEI initiatives may differ from their perceived impact on the workplace. This study is a first step towards increasing transparency and informing AI/ML practitioners and organizations with empirical results. We aim to foster equitable decision-making in the design and evaluation of organizational policies and provide data that may empower professionals to make more informed choices of prospective workplaces.

cs.CY↗