By distributing transactions across different shards for parallel processing, sharding improves the scalability of blockchain systems. When transactions involve multiple shards, coordinated execution is required, making the efficiency of cross-shard transactions (CTXs) a critical determinant of overall system performance. However, existing CTX protocols lack pre-execution state verifiability in the mempool, forcing target shards to defer transaction processing until source shard confirmation. Moreover, the tightly coupled source and target execution pipeline serializes CTXs processing across shards, significantly restricting parallel execution of CTXs. To address these issues, this paper proposes ParaShard, a high throughput and low latency CTX protocol based on a fast path parallel execution. ParaShard decouples transaction acceptance from on-chain commitment by enabling source shards to generate off-chain threshold signature lock proofs, which can be directly verified by target shards. As a result, target shards can admit CTXs into their local transaction pool without waiting for source shard consensus, enabling early processing and parallel execution across shards. Moreover, we implement a prototype of ParaShard and evaluate it using historical Ethereum transactions. Experimental results demonstrate that, compared with state-of-the-art protocols such as X-Shard and Monoxide, ParaShard achieves 30.5
Mobile access within public finance and enterprise environments often requires lightweight anonymous authentication, allowing users to prove authorization without disclosing their identities. Group Time-based One-Time Passwords (GTOTP) has recently been proposed as a lightweight primitive meeting this need with post-quantum security. To address dynamic group membership, Cao et al. introduced DGTOne, the first dynamic GTOTP construction. It employs chameleon hashes to precompute a fixed set of Merkle-tree leaves (mount points), into which conventional TOTP verification points (VPs) contributed by group members are adaptively inserted. However, DGTOne partitions mount points by time epochs, so they can expire and become unusable, causing capacity waste due to unpredictable join times. Moreover, its outsourced proof generation requires verifiers to be online each epoch to fetch refreshed credentials from Registration Authority (RA), defeating offline verification needed in mobile access. We address these limitations with two new schemes. First, we propose , a no-wastage DGTOTP design that constructs Merkle trees of members’ verification points (VP-trees) on demand, eliminating expired mount points at the cost of added handling latency. To mitigate this latency, we introduce , which instantiates multiple small one-time signature (OTS) trees whose leaves (OTS public keys) serve as mount points. New members’ VPs are signed immediately using unused leaves, achieving low wastage. We formally prove that the wastage rate of is, with overwhelming probability, lower than that of DGTOne. By modeling the registration process and optimizing OTS-tree size, for deployments with up to 500 members (209 initially, 20 added monthly), reduces mount point wastage rate by 10.2
In this work, we propose a novel image steganography framework based on adaptive embedding of a secret image into a cover image using a Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). The system dynamically selects optimal embedding zones and enhances the visual and structural fidelity of the reconstructed secret image Unlike conventional approaches that rely on fixed embedding regions or uniform coefficient allocation, the proposed approach adopts a content-aware, block-based embedding strategy that jointly optimizes imperceptibility, robustness, and payload capacity. For each pair of secret and cover blocks, transformed and quantized frequency coefficients are analyzed by a trained CNN to identify the most suitable insertion area based on texture complexity and spectral characteristics. To ensure confidentiality and strengthen resistance to steganalytic attacks, the coefficients of the secret blocks are encrypted using a lightweight and efficient algorithm prior to embedding, guaranteeing that the hidden information remains unintelligible even if successfully extracted. Additionally, the GAN-based refinement module is used to enhance the visual quality of the restored secret image. Experimental evaluations demonstrate that the proposed model achieves high stego-image quality, strong resilience to compression and noise attacks, and accurate secret-image reconstruction.
As communication infrastructures migrate towards post-quantum cryptography, Mersenne number-based AJPS cryptosystems have emerged as promising lightweight candidates for resource-constrained environments, including IoT networks. These schemes offer high arithmetic efficiency, yet their security relies heavily on the assumed hardness of inherent mathematical problems. In this paper, we evaluate the resilience of these primitives against advanced cryptanalytic threats. We propose novel attacks that leverage continued fractions and lattice-based strategy for modular polynomial equations. Crucially, we demonstrate that the computational complexity of our proposed attacks is polynomial in the bit-length of the modulus and is independent of the keys’ Hamming weight. We significantly relax the size constraints on the keys, proving that the weak key space is much larger than estimated. We validate these findings through extensive numerical experiments, confirming that secret keys can be recovered in seconds. Our results indicate that current parameter selections for Mersenne number-based AJPS schemes are insufficient for long-term data protection, necessitating a rigorous re-evaluation of security margins.
We present Sinba, an optimized realization of Vectorized Batch Private Information Retrieval (VB-PIR) built on RLWE-based homomorphic encryption with SIMD batching. While prior VB-PIR systems significantly reduce communication by packing queries and merging responses across buckets, their server-side online latency is often dominated by rotation-heavy packed inner products executed multiple times (e.g., slot selection and aggregation). Sinba targets this bottleneck through three complementary techniques. First, it encodes database records into a two-dimensional slot layout, mapping multi-slot payloads onto multiple plaintext rows with regular structure. Second, it rewrites the dominant rotate-multiply-accumulate computation as a 2D baby-step/giant-step (BSGS) schedule, enabling reuse of baby-step rotations across rows and supporting an unbalanced parameter choice tuned to the number of rows, thereby reducing rotations from (N) to approximately (√(N)) . Third, Sinba shifts rotation alignment into offline preprocessing, storing the encoded database in NTT form to accelerate ciphertext-plaintext products in the online phase. We evaluate across batch size, database size, and payload length. On a database of 2^20 items with 256-byte payloads, Sinba reduces server response latency by 8.5-13.3× compared to the VB-PIR baseline of Mughees and Ren (IEEE S P 2023), while preserving the communication advantages of vectorization.
