IACR News item: 17 March 2025
Dev Mehta, Trey Marcantino, Mohammad Hashemi, Sam Karkache, Dillibabu Shanmugam, Patrick Schaumont, Fatemeh Ganji
Side-channel analysis (SCA) is a growing field in
hardware security where adversaries extract secret information
from embedded devices by measuring physical observables like
power consumption and electromagnetic emanation. SCA is a
security assessment method used by governmental labs, standardization
bodies, and researchers, where testing is not just
limited to standardized cryptographic circuits, but it is expanded
to AI accelerators, Post Quantum circuits, systems, etc. Despite
its importance, SCA is performed on an ad hoc basis in the
sense that its flow is not systematically optimized and unified
among labs. As a result, the current solutions do not account
for fair comparisons between analyses. Furthermore, neglecting
the need for interoperability between datasets and SCA metric
computation increases students’ barriers to entry. To address
this, we introduce SCAPEgoat, a Python-based SCA library
with three key modules devoted to defining file format, capturing
interfaces, and metric calculation. The custom file framework
organizes side-channel traces using JSON for metadata, offering
a hierarchical structure similar to HDF5 commonly applied in
SCA, but more flexible and human-readable. The metadata can
be queried with regular expressions, a feature unavailable in
HDF5. Secondly, we incorporate memory-efficient SCA metric
computations, which allow using our functions on resource-restricted
machines. This is accomplished by partitioning datasets
and leveraging statistics-based optimizations on the metrics. In
doing so, SCAPEgoat makes the SCA more accessible to newcomers
so that they can learn techniques and conduct experiments
faster and with the possibility to expand on in the future.
Additional news items may be found on the IACR news page.