arXiv subjects

Kyle Yates

Publications and source records attributed to Kyle Yates.

4 recordsLinked to original sources

arXiv2026

On the Feasibility of Hybrid Homomorphic Encryption for Intelligent Transportation Systems

Many Intelligent Transportation Systems (ITS) applications require strong privacy guarantees for both users and their data. Homomorphic encryption (HE) enables computation directly on encrypted messages and thus offers a compelling approach to privacy-preserving data processing in ITS. However, practical HE schemes incur substantial ciphertext expansion and communication overhead, which limits their suitability for time-critical transportation systems. Hybrid homomorphic encryption (HHE) addresses this challenge by combining a homomorphic encryption scheme with a symmetric cipher, enabling efficient encrypted computation while dramatically reducing communication cost. In this paper, we develop theoretical models of representative ITS applications that integrate HHE to protect sensitive vehicular data. We then perform a parameter-based evaluation of the HHE scheme Rubato to estimate ciphertext sizes and communication overhead under realistic ITS workloads. Our results show that HHE achieves orders-of-magnitude reductions in ciphertext size compared with conventional HE while maintaining cryptographic security, making it significantly more practical for latency-constrained ITS communication.

cs.CR↗

arXiv2025

Security Analysis of Integer Learning with Errors with Rejection Sampling

At ASIACRYPT 2018, a digital attack based on linear least squares was introduced for a variant of the learning with errors (LWE) problem which omits modular reduction known as the integer learning with errors problem (ILWE). In this paper, we present a theoretical and experimental study of the effectiveness of the attack when applied directly to small parameter ILWE instances found in popular digital signature schemes such as CRYSTALS-Dilithium which utilize rejection sampling. Unlike other studies which form ILWE instances based on additional information obtained from side-channel attacks, we take a more direct approach to the problem by constructing our ILWE instance from only the obtained signatures. We outline and introduce novel techniques in our simulation designs such as modular polynomial arithmetic via matrices in $\mathbb{R}$, as well as algorithms for handling large sample sizes efficiently. Our experimental results reinforce the proclaimed security of signature schemes based on ILWE. We additionally discuss the implications of our work and digital signatures as a whole in regards to real-world applications such as in Intelligent Transportation Systems (ITS).

cs.CR↗

arXiv2025

Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving I2I Communication in ITS

This study experimentally evaluates the feasibility of post-quantum secure Homomorphic Encryption (HE) for privacy-preserving Infrastructure-to-Infrastructure (I2I) communication in Intelligent Transportation Systems (ITS). Unlike prior simulation-based efforts, this work implements three lattice-based HE schemes: Brakerski-Fan-Vercauteren (BFV), Brakerski-Gentry-Vaikuntanathan (BGV), and Cheon-Kim-Kim-Song (CKKS), within a real experimental pipeline representing roadside unit (RSU)-Cloud data exchange over Wi-Fi and Ethernet networks. The experiments benchmark encrypted addition and addition-plus-multiplication operations representing key analytical tasks, such as vehicle queue assessment and regional speed computation. Results show that while BFV achieves sub-5-second latency suitable for intersection-level analytics, BGV supports regional aggregation with 10 to 30-second updates. CKKS, though exhibiting higher latency (21-32 seconds), remains practical for minute-scale applications like eco-driving. These findings demonstrate that post-quantum HE can enable privacy-preserving ITS backhaul analytics when latency requirements align with application needs. The study also presents optimization pathways, including algorithmic tuning, network adaptation, and hardware acceleration, to reduce end-to-end delay.

cs.CR↗

arXiv2024

On the Hardness of the $L_1-L_2$ Regularization Problem

The sparse linear reconstruction problem is a core problem in signal processing which aims to recover sparse solutions to linear systems. The original problem regularized by the total number of nonzero components (also known as $L_0$ regularization) is well-known to be NP-hard. The relaxation of the $L_0$ regularization by using the $L_1$ norm offers a convex reformulation, but is only exact under certain conditions (e.g., restricted isometry property) which might be NP-hard to verify. To overcome the computational hardness of the $L_0$ regularization problem while providing tighter results than the $L_1$ relaxation, several alternate optimization problems have been proposed to find sparse solutions. One such problem is the $L_1-L_2$ minimization problem, which is to minimize the difference of the $L_1$ and $L_2$ norms subject to linear constraints. This paper proves that solving the $L_1-L_2$ minimization problem is NP-hard. Specifically, we prove that it is NP-hard to minimize the $L_1-L_2$ regularization function subject to linear constraints. Moreover, it is also NP-hard to solve the unconstrained formulation that minimizes the sum of a least squares term and the $L_1-L_2$ regularization function. Furthermore, restricting the feasible set to a smaller one by adding nonnegative constraints does not change the NP-hardness nature of the problems.

math.OC↗