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Differential Fault Analysis on A.E.S.
Get PDFThis paper investigates Differential Fault Analysis (DFA) on the Advanced Encryption Standard (AES). Following the Bellcore fault model [2], we assume the injection of a single-byte error during the final rounds of encryption.
We show how such faults propagate through AES transformations and demonstrate that the last round key can be derived with only a small number of faulty ciphertexts. From this information, the original cipher key can be efficiently reconstructed. Numerical simulations confirm the practicality of the attack, requiring fewer than ten faulty outputs to recover a complete subkey. Our results highlight the vulnerability of AES implementations in tamper-resistant devices, such as smart cards, when exposed to fault injections.
These findings emphasize the importance of incorporating robust error-detection and fault-resilient countermeasures in secure hardware
CL-SCA: A Contrastive Learning Approach for Profiled Side-Channel Analysis
Get PDFSide-channel analysis (SCA) based on machine learning, particularly neural networks, has gained considerable attention in recent years. However, previous works predominantly focus on establishing connections between labels and related profiled traces. These approaches primarily capture label-related features and often overlook the connections between traces of the same label, resulting in the loss of some valuable information. Besides, the attack traces also contain valuable information that can be used in the training process to assist model learning. In this paper, we propose a profiled SCA approach based on contrastive learning named CL-SCA to address these issues. This approach extracts features by emphasizing the similarities among traces, thereby improving the effectiveness of key recovery while maintaining the advantages of the original SCA approach. Through experiments of different datasets from different platforms, we demonstrate that CL-SCA significantly outperforms other approaches. Moreover, by incorporating attack traces into the training process using our approach, we can further enhance its performance. This extension can improve the effectiveness of key recovery, which is fully verified through experiments on different datasets
Information-Theoretically Secure Distributed Protocols for Two-Party MPC Primitives
Get PDFWe study two-party secure computation in a \emph{distributed mediated} model with servers.
We present \emph{information-theoretically secure} protocols for foundational primitives—
-out-of- and -out-of- oblivious transfer (OT), Priced OT, Generalized OT, and
Oblivious Multivariate Polynomial Evaluation (OMPE)—that (i) enforce receiver correctness,
(ii) support an offline sender after setup, and (iii) achieve robustness against malicious
receivers and malicious servers under honest-majority assumptions.
Our approach reduces each primitive to a distributed scalar-product core combined with a
\emph{distributed vector-validation} (DVV) mechanism that prevents unauthorized
linear-combination leakage while preserving privacy. We prove unconditional privacy and
robustness in the semi-honest setting and extend the guarantees to the malicious-server
setting, tolerating up to or corrupt servers depending on the variant.
The constructions are modular, require only lightweight client work (sharing and
reconstruction), and allow the sender’s one-time upload to serve multiple, independent
receivers without further interaction.
We analyze the round and communication complexity of our protocols and provide empirical
evidence of practical efficiency. The mediated setting naturally fits privacy-preserving
services such as collaborative filtering, distributed constraint optimization, voting, and
federated analytics. Collectively, our results show that distributed mediation yields simple,
scalable, and unconditionally private realizations of core two-party MPC functionalities
Blockchain Governance via Sharp Anonymous Multisignatures
Get PDFElectronic voting has occupied a large part of the cryptographic protocols literature. The recent reality of blockchains---in particular, their need for online governance mechanisms---has brought new parameters and requirements to the problem. We identify the key requirements of a blockchain governance mechanism, namely correctness (including eliminative double votes), voter anonymity, and traceability, and investigate mechanisms that can achieve them with minimal interaction and under assumptions that fit the blockchain setting.
First, we define a signature-like primitive, which we term \textit{sharp anonymous multisignatures} (in short, AMS) that tightly meets the needs of blockchain governance. In a nutshell, AMSs allow any set of parties to generate a signature, e.g., on a proposal to be voted upon, which, if posted on the blockchain, hides the identities of the signers/voters but reveals their number. This can be seen as a (strict) generalization of threshold ring signatures (TRS).
We next turn to constructing such AMSs and using them in various governance scenarios---e.g., single vote vs. multiple votes per voter. In this direction, although the definition of TRS does not imply AMS, one can compile some existing TRS constructions into AMS. This raises the question: What is the TRS structure that allows such a compilation?
To answer the above, we devise templates for TRSs. Our templates encapsulate and abstract the structure that allows for the above compilation---most of the TRS schemes that can be compiled into AMS are, in fact, instantiations of our template. This abstraction makes our template generic for instantiating TRSs and AMSs from different cryptographic assumptions (e.g., DDH, LWE, etc.). One of our templates is based on chameleon hashes, and we explore a framework of lossy chameleon hashes to understand their nature fully.
Finally, we turn to how AMS schemes can be used in our applications. We provide fast (in some cases non-interactive) AMS-based blockchain governance mechanisms for a wide spectrum of assumptions on the honesty (semi-honest vs malicious) and availability of voters and proposers
Fabric-X: Scaling Hyperledger Fabric for Asset Exchange
Get PDFThe adoption of Distributed Ledger Technology (DLT) for critical
financial infrastructures like Central Bank Digital Currencies (CB-
DCs) is hindered by a significant performance gap. Permissioned
blockchains such as Hyperledger Fabric, while conceptually suit-
able, are limited by architectural bottlenecks in their monolithic
peer design and consensus mechanisms, preventing them from
achieving the required scale.
This paper presents a fundamental re-architecture of Hyper-
ledger Fabric that addresses these challenges end-to-end. We de-
compose the monolithic peer into independently scalable microser-
vices for endorsement, validation, and committing. To maximize
parallelism, we introduce a transaction dependency graph that en-
ables the safe, concurrent validation of transactions across multiple
blocks. Complementing the peer redesign, we introduce Arma, a
novel sharded Byzantine Fault Tolerant (BFT) ordering service that
dramatically increases throughput by ordering compact transaction
digests rather than full transaction payloads. We implemented and
benchmarked this framework with a UTXO-based CBDC applica-
tion. Our evaluation demonstrates a peak throughput exceeding
200,000 transactions per second (TPS)—a two-orders-of-magnitude
improvement over the standard implementation. This work proves
that permissioned DLTs can be engineered for national-scale pay-
ment systems, providing a resilient and highly performant foun-
dation for practical CBDC deployments and the integration of ad-
vanced, computationally intensive features
Is ML-Based Cryptanalysis Inherently Limited? Simulating Cryptographic Adversaries via Gradient-Based Methods
Get PDFGiven the recent progress in machine learning (ML), the cryptography community has started exploring the applicability of ML methods to the design of new cryptanalytic approaches. While current empirical results show promise, the extent to which such methods may outperform classical cryptanalytic approaches is still somewhat unclear.
In this work, we initiate exploration of the theory of ML-based cryptanalytic techniques, in particular providing new results towards understanding whether they are fundamentally limited compared to traditional approaches. Whereas most classic cryptanalysis crucially relies on directly processing individual samples (e.g., plaintext-ciphertext pairs), modern ML methods thus far only interact with samples via gradient-based computations that average a loss function over all samples. It is, therefore, conceivable that such gradient-based methods are inherently weaker than classical approaches.
We introduce a unifying framework for capturing both ``sample-based\u27\u27 adversaries that are provided with direct access to individual samples and ``gradient-based\u27\u27 ones that are restricted to issuing gradient-based queries that are averaged over all given samples via a loss function. Within our framework, we establish a general feasibility result showing that any sample-based adversary can be simulated by a seemingly-weaker gradient-based one. Moreover, the simulation exhibits a nearly optimal overhead in terms of the gradient-based simulator\u27s running time. Finally, we extend and refine our simulation technique to construct a gradient-based simulator that is fully parallelizable (crucial for avoiding an undesirable overhead for parallelizable cryptanalytic tasks), which is then used to construct a gradient-based simulator that executes the particular and highly useful gradient-descent method.
Taken together, although the extent to which ML methods may outperform classical cryptanalytic approaches is still somewhat unclear, our results indicate that such gradient-based methods are not inherently limited by their seemingly restricted access to the provided samples
GRASP: Accelerating Hash-based PQC Performance on GPU Parallel Architecture
Get PDF, one of the Post-Quantum Cryptography Digital Signature Algorithms (PQC-DSA) selected by NIST in the third round, features very short public and private key lengths but faces significant performance challenges compared to other post-quantum cryptographic schemes, limiting its suitability for real-world applications. To address these challenges, we propose the GPU-based paRallel Accelerated (GRASP), which leverages GPU technology to enhance the efficiency of signing and verification processes. We propose an adaptable parallelization strategy for , analyzing its signing and verification processes to identify critical sections for efficient parallel execution. Utilizing CUDA, we perform bottom-up optimizations, focusing on memory access patterns and hypertree computation, to enhance GPU resource utilization. These efforts, combined with kernel fusion technology, result in significant improvements in throughput and overall performance. Extensive experimentation demonstrates that our optimized CUDA implementation of achieves superior performance. Specifically, our GRASP scheme delivers throughput improvements ranging from 1.37× to 3.45× compared to state-of-the-art GPU-based solutions and surpasses the NIST reference implementation by over three orders of magnitude, highlighting a significant performance advantage
A Succinct Range Proof for Polynomial-based Vector Commitment
Get PDFRange proofs serve as a protocol for the prover to prove to the verifier that a committed number resides within a specified range, such as , without disclosing the actual value. These proofs find extensive application in various domains, including anonymous cryptocurrencies, electronic voting, and auctions. However, the efficiency of many existing schemes diminishes significantly when confronted with batch proofs encompassing multiple elements.
The pivotal challenge arises from their focus on the commitment to a singular element rather than a vector. Addressing this gap, our paper introduces MissileProof, a zero-knowledge, succinct, non-interactive argument of knowledge tailored for the range proof of a vector commitment. Our core contribution lies in reducing this argument to a bi-to-uni variate SumCheck problem and the bivariate polynomial ZeroTest problem, and design two polynomial interactive oracle proofs (PIOPs) for each problem.
Our principal innovation involves the transformation of this argument into a bi-to-uni variate SumCheck problem and the bivariate polynomial ZeroTest problem. To tackle these challenges, we devise two Polynomial Interactive Oracle Proofs (PIOPs) for each problem.
As far as we know, our scheme has the optimal proof size (), the optimal statement length (), and the optimal verification time (), at the expense of slightly sacrificing proof time ( operations on the prime field for FFT and group exponentiations in ). We prove the security of this scheme. Experimental data shows for a committed vector of length and , our work has the best performance in terms of the statement length (0.03125KB), proof size (1.375KB) and verification time (0.01s) with a slightly increased proof time (614s)
An NVMe-based Secure Computing Platform with FPGA-based TFHE Accelerator
Get PDFIn this study, we introduce a new approach to secure computing by implementing a platform that utilizes a non-volatile memory express (NVMe)-based system with an FPGA-based Torus fully homomorphic encryption (TFHE) accelerator, solid state drive (SSD), and middleware on the host-side. Our platform is the first to offer completely secure computing capabilities for TFHE by using an FPGA-based accelerator. We defined secure computing instructions to evaluate 14-bit to 14-bit functions using TFHE. Our middleware allows for the communication of ciphertexts, keys, and secure computing programs while invoking secure computing programs through NVMe commands with metadata. Our performance evaluation demonstrates that our secure computing platform outperforms CPU-based and GPU-based platforms by 15 to 120 times and 2.5 to 3 times, respectively, in gate bootstrapping execution time. Additionally, our platform uses 7 to 12 times less electric energy consumption during the gate bootstrapping execution time than CPU-based platforms and 4.95 times less than a GPU-based platform. The performance of a machine learning application running on our platform shows that bootstrapping accounts for more than 80% of ciphertext learning time
Relativized Succinct Arguments in the ROM Do Not Exist
Get PDFA relativized succinct argument in the random oracle model (ROM) is a succinct argument in the ROM that can prove/verify the correctness of computations that involve queries to the random oracle. We prove that relativized succinct arguments in the ROM do not exist. The impossibility holds even if the succinct argument is interactive, and even if soundness is computational (rather than statistical).
This impossibility puts on a formal footing the commonly-held belief that succinct arguments require non-relativizing techniques. Moreover, our results stand in sharp contrast with other oracle models, for which a recent line of work has constructed relativized succinct non-interactive arguments (SNARGs). Indeed, relativized SNARGs are a powerful primitive that, e.g., can be used to obtain constructions of IVC (incrementally-verifiable computation) and PCD (proof-carrying data) based on falsifiable cryptographic assumptions. Our results rule out this approach for IVC and PCD in the ROM