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Jad Zahreddine

Publications and source records attributed to Jad Zahreddine.

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Exploiting Load/Store Leakage of Sparse Vectors for Key Recovery in HQC

Hamming Quasi-Cyclic (HQC) is a code-based key encapsulation mechanism selected by NIST for standardization, making its resistance to implementation attacks critically important. We present a side-channel attack that exploits load/store leakage in the manipulation of HQC's sparse secret vectors. Analysing Cortex-M4 assembly generated from the reference implementation, we identify a leakage surface in which the low and high 32-bit halves of each 64-bit word leak with different strengths, due to compiler-generated register spilling. We exploit this leakage to construct a simple zero-word distinguisher classifying machine words of the secret vector as zero or nonzero from electromagnetic measurements. The recovered zero positions are then translated into decoding hints, reducing HQC key recovery to a shortened syndrome-decoding problem. We analyse the resulting decoding complexity for all HQC parameter sets: at 32-bit granularity an expected 88.7% of the machine words of y are zero for HQC-1, cutting the decoding to $\approx$ 2 46 bit operations. Experiments on a Cortex-M4 validate the predicted low/high-half asymmetry-approximately 500 traces for the stronger low-half channel and 5,000 for the weaker high-half channeland recover the zero words of an HQC-1 key at 32-bit granularity. Finally, we discuss practical countermeasures that eliminate the sparsity exploited by the attack.

cs.CR

Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work mainly aimed at establishing proof of principle. In the present document we describe new algorithms that can handle complex realistic detectors. The new algorithms are implemented in ACTS, a common framework for tracking software. This work aims at implementing a realistic GNN-based algorithm that can be deployed in an HL-LHC experiment.

physics.ins-det