The Learning with Rounding (LWR) problem, introduced as a deterministic variant of Learning with Errors (LWE), has become a promising foundation for post-quantum cryptography. This Systematization of Knowledge (SoK) article presents a comprehensive survey of the theoretical foundations, algorithmic developments, and practical implementations of LWR-based cryptographic schemes. We introduce LWR within the broader landscape of lattice-based cryptography and post-quantum security, highlighting its advantages such as reduced randomness, improved efficiency, and enhanced side-channel resistance. We explore the evolution of security reductions from LWR to LWE, including recent advances that support practical parameter regimes and address challenges in both bounded and unbounded sample settings. This article systematically reviews existing LWR-based schemes — including Saber, Lizard, Florete, Espada, Sable, and SMAUG — analyzing their design choices, parameter sets, and performance tradeoffs. Furthermore, we examine the impact of LWR on side-channel resistance, failure probabilities, and masking efficiency, demonstrating its suitability for secure and efficient implementations. By consolidating the research spanning theory and practice, this SoK aims at guiding future cryptographic design and standardization efforts leveraging LWR.

In this work, we present a systematic study of Side-Channel Attacks (SCA) and Fault Injection Attacks (FIA) on structured lattice-based schemes, with main focus on Kyber Key Encapsulation Mechanism (KEM) and Dilithium signature scheme, which are leading candidates in the NIST standardization process for Post-Quantum Cryptography (PQC). Through our study, we attempt to understand the underlying similarities and differences between the existing attacks while classifying them into different categories. Given the wide variety of reported attacks, simultaneous protection against all the attacks requires to implement customized protections/countermeasures for both Kyber and Dilithium. We therefore present a range of customized countermeasures, capable of providing defenses/mitigations against existing SCA/FIA, and incorporate several SCA and FIA countermeasures within a single design of Kyber and Dilithium. Among the several countermeasures discussed in this work, we present novel countermeasures that offer simultaneous protection against several SCA- and FIA-based chosen-ciphertext attacks for Kyber KEM. We implement the presented countermeasures within two well-known public software libraries for PQC: (1) pqm4 library for the ARM Cortex-M4-based microcontroller and (2) liboqs library for the Raspberry Pi 3 Model B Plus based on the ARM Cortex-A53 processor. Our performance evaluation reveals that the presented custom countermeasures incur reasonable performance overheads on both the evaluated embedded platforms. We therefore believe our work argues for usage of custom countermeasures within real-world implementations of lattice-based schemes, either in a standalone manner or as reinforcements to generic countermeasures such as masking.

Fully Homomorphic Encryption (FHE) is a technique that allows computation on encrypted data. It has the potential to drastically change privacy considerations in the cloud, but high computational and memory overheads are preventing its broad adoption. TFHE is a promising Torus-based FHE scheme that heavily relies on bootstrapping, the noise-removal tool invoked after each encrypted logical/arithmetical operation.

Homomorphic encryption (HE) enables calculating on encrypted data, which makes it possible to perform privacy-preserving neural network inference. One disadvantage of this technique is that it is several orders of magnitudes slower than calculation on unencrypted data. Neural networks are commonly trained using floating-point, while most homomorphic encryption libraries calculate on integers, thus requiring a quantisation of the neural network. A straightforward approach would be to quantise to large integer sizes (e.g. 32 bit) to avoid large quantisation errors. In this work, we reduce the integer sizes of the networks, using quantisation-aware training, to allow more efficient computations. For the targeted MNIST architecture proposed by Badawi et al. [1], we reduce the integer sizes by 33% without significant loss of accuracy, while for the CIFAR architecture, we can reduce the integer sizes by 43%. Implementing the resulting networks under the BFV homomorphic encryption scheme using SEAL, we could reduce the execution time of an MNIST neural network by 80% and by 40% for a CIFAR neural network.

Masked comparison is one of the most expensive operations in side-channel secure implementations of lattice-based post-quantum cryptography, especially for higher masking orders. First, we introduce two new masked comparison algorithms, which improve the arithmetic comparison of D’Anvers et al. (2021) and the hybrid comparison method of Coron et al. (2021) respectively. We then look into implementation-specific optimizations, and show that small specific adaptations can have a significant impact on the overall performance. Finally, we implement various state-of-the-art comparison algorithms and benchmark them on the same platform (ARM-Cortex M4) to allow a fair comparison between them. We improve on the arithmetic comparison of D’Anvers et al. with a factor$\approx 20\%$by using Galois Field multiplications and the hybrid comparison of Coron et al. with a factor$\approx 25\%$by streamlining the design. Our implementation-specific improvements allow a speedup of a straightforward comparison implementation of$\approx 33\%$. We discuss the differences between the various algorithms and provide the implementations and a testing framework to ease future research.

The candidates for the NIST Post-Quantum Cryptography standardization have undergone extensive studies on efficiency and theoretical security, but research on their side-channel security is largely lacking. This remains a considerable obstacle for their real-world deployment, where side-channel security can be a critical requirement. This work describes a side-channel-resistant instance of Saber, one of the lattice-based candidates, using masking as a countermeasure. Saber proves to be very efficient to masking due to two specific design choices: power-of-two moduli and limited noise sampling of learning with rounding. A major challenge in masking lattice-based cryptosystems is the integration of bit-wise operations with arithmetic masking, requiring algorithms to securely convert between masked representations. The described design includes a novel primitive for masked logical shifting on arithmetic shares and adapts an existing masked binomial sampler for Saber. An implementation is provided for an ARM Cortex-M4 microcontroller, and its side-channel resistance is experimentally demonstrated. The masked implementation features a 2.5x overhead factor, significantly lower than the 5.7x previously reported for a masked variant of NewHope. Masked key decapsulation requires less than 3,000,000 cycles on the Cortex-M4 and consumes less than 12kB of dynamic memory, making it suitable for deployment in embedded platforms.