DigitalSpace (United States)

With the advancement in quantum computing technology, the number theory-based hard problems underlying traditional searchable encryption algorithms are now vulnerable to efficient quantum attacks. To address this challenge, this paper proposes Dilithium-PAEKS (Dilithium-Public Authenticated Encryption with Keyword Search), a searchable encryption scheme based on the post-quantum cryptographic algorithm CRYSTALS-Dilithium. By transforming the verification relationship of digital signatures into a matching relationship between trapdoors and ciphertexts, the scheme not only meets the functional requirements of searchable encryption but also demonstrates quantum resistance. The implementation enhances algorithm efficiency through keyword-based signatures and dynamic matching testing mechanisms. The security of the scheme is defined by the MLWE and MSIS hard problems, with proofs of keyword ciphertext indistinguishability and trapdoor indistinguishability under the random oracle model. Additionally, the scheme provides strong resistance against both outside and insider keyword guessing attacks through sender–receiver binding mechanisms and trapdoor indistinguishability properties. Experimental results show that, compared to the post-quantum schemes CP-Absel and LB-FSSE, the proposed scheme demonstrates superior overall computational efficiency while maintaining stronger quantum resistance than the traditional scheme SM9-PAEKS.

Direct measurements and satellite-derived estimates of Net Ecosystem Exchange (NEE) provide a robust, ecosystem-scale view of land–atmosphere CO₂ dynamics, yet they are rarely connected to product-level carbon accounting frameworks used in policy and markets. This contribution presents a practical, ISO-aligned methodology for integrating NEE into Life Cycle Assessment (LCA) and Product Carbon Footprints, bridging land-based flux measurements with product-based MRV.The approach replaces generic cultivation-stage emission factors with site-specific NEE data, while preserving full compliance with ISO 14040/44 and ISO 14067. A core boundary-based decision rule ensures carbon mass balance and prevents double counting by explicitly accounting for the fate of harvested biomass carbon. The method is demonstrated across multiple real-world case studies, including oil palm, livestock feed systems, and tobacco production, using both satellite-based and multi-year averaged NEE data.Results show that integrating NEE substantially improves the climate relevance of product footprints, enabling year-on-year tracking of land management performance and revealing carbon footprint reductions of 19–47% relative to conventional LCAs. Beyond accounting, the framework enables direct translation of flux measurements into decision-relevant indicators for insetting, land management optimization, and supply-chain MRV. The work illustrates how flux science can move from research contexts into scalable, auditable applications with clear societal and market benefits.

State-of-the-art watermarking and fingerprinting techniques for Large Language Models (LLMs) are explored, with our analysis spanning a wide array of methodologies designed to protect the intellectual property of LLMs. The review of watermarking techniques is based on embedding watermarks during the training, logits generation, and token sampling phases. Meanwhile, we investigate the application of watermarking technology in multimodal LLMs and potential attacks on watermarks. Moreover, our examination of fingerprinting techniques revealed the ingenuity behind methods used to identify LLMs. We discussed the development of fingerprints based on model behavior and using deep learning models to learn thresholds for fingerprint comparison. Our survey has underscored the importance of advancing security measures for LLMs, especially in light of the increasing sophistication of adversarial attacks. As LLMs continue to play a pivotal role in advancing AI technologies, developing and refining security measures that safeguard their intellectual property and ensure their ethical deployment is imperative.

Storage savings and data confidentiality are two primary yet conflicting goals in outsourced backup management. While deduplication-aware encryption has been extensively studied to make deduplication viable for encrypted data, it is incompatible with fine-grained delta and local compression for further storage savings. We present ShieldReduce, a secure outsourced storage system that aims for fine-grained shielded data reduction by applying deduplication, delta compression, and local compression to data in a trusted execution environment based on Intel SGX, so as to achieve high storage savings with security guarantees. To mitigate the I/Os of accessing base chunks for delta compression in SGX, ShieldReduce adopts bi-directional delta compression via a novel hybrid inline and offline compression design to maintain the physical locality of base chunks. It also offers a storage-prioritized offline compression mode for further storage savings and ensures crash consistency during data reduction. Evaluation on various backup workloads shows that ShieldReduce achieves significant speedups over a shielded baseline without bi-directional delta compression, while maintaining comparable storage savings to fine-grained data reduction for plain data.

Δ1-pyrroline-5-carboxylate (P5C) reductase 1 (PYCR1) catalyzes the NAD(P)H-dependent conversion of L-P5C to L-proline and is one of the most consistently up-regulated metabolic enzymes in cancer cells. High PYCR1 expression is associated with adverse clinical outcomes, and its knockdown inhibits tumor proliferation and metastasis, motivating inhibitor discovery. All structurally validated PYCR1 inhibitors to date bind in the active site and are anchored in the L-P5C binding pocket by an anionic functional group, typically carboxylate. Seeking inhibitors with alternative anchors, we used X-ray crystallography to screen 22 fragment-like compounds (MW=189-343 Da) from docking that represent six different carboxylic acid isosteres. Surprisingly, only one compound bound in the active site. Four other compounds were found in three adjacent remote sites located in oligomer interfaces. The compounds bind 7 Å from NADH and 10-14 Å from L-P5C, and the intervening space is blocked by protein for inhibitors in Sites 1A/1B and open for inhibitors in Site 2. Together, the three binding sites define a ligand binding hot spot groove that spans 33 Å. The remote binders inhibit PYCR1 activity with K values from the mixed model of inhibition of 32μM to 2mM. Co-crystal structures of PYCR1 with combinations of allosteric inhibitors, NADH, and L-P5C/proline analogs suggest the inhibitors can bind to the ternary PYCR1-L-P5C-NAD(P)H complex in addition to the free enzyme, consistent with a mixed mechanism of inhibition. The discovery of an allosteric inhibitor binding groove that accommodates multiple fragments heralds a new era of PYCR1 inhibitor design.

The Android operating system, dominating over 85% of the mobile market through open-source flexibility, suffers from intrinsic vulnerabilities. The APK(Android Package Kit) parsability and Smali code modifiability enable attackers to decompile applications via tools. This facilitates widespread repackaging-malicious actors inject payloads or tamper with functionality, redistributing counterfeit applications(apps) through third-party markets. These practices cause dual damage. Developers face code theft and revenue diversion, while users endure privacy leaks, financial fraud, and device compromise. Consequently, accurate repackaging detection has become critical. This paper reviews recent progress in repackaging detection techniques for Android applications. We first outline the fundamental characteristics of Android apps and then examine detection methods based on code analysis and resource similarity. Frequently used Android app datasets and evaluation metrics for measuring the effectiveness of repackaging detection methods are also summarized. Finally, we discuss the development trends of repackaging detection techniques and identify future research directions, with the aim of providing meaningful insights and guidance for researchers in this domain.

The performance of network intrusion detection systems (IDS) critically depends on the completeness of traffic feature representation. Existing methods predominantly focus either on intra-flow features (e.g., packet length sequences) or single-dimensional inter-flow relationships, limiting optimal classification. To address this restriction, we propose IF<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-STG to enhance intrusion detection via interpretable inter-flow spatio-temporal graphs and intra-flow features. The key innovation of our scheme is the construction of an Inter-flow Spatio-Temporal relationship Graph (ISTG), with two-dimensional edges explicitly quantifying the correlation strength between flows. On this basis, a GNN learns the inter-flow context. In parallel, a CNN extracts fine-grained intra-flow features from raw bytes of individual flows. Finally, the two modal features are fused to acquire a comprehensive representation. This representation can effectively capture both intrinsic behavior patterns of flows and contextual inter-flow relationships, thereby significantly enhancing the detection performance and robustness of model. Experimental results show that the proposed IF<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-STG achieves over 99% accuracy on three real-world datasets, outperforming existing approaches. The constructed ISTG provides explicit visual interpretation of flow dependencies. More importantly, IF<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-STG-Inter, using only inter-flow features, maintains 97.78% detection accuracy in cross-dataset tests involving time-disparate datasets, validating superior generalization of inter-flow spatio-temporal features. This advantage renders it particularly promising for practical intrusion detection applications.