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检索条件"丛书名=Lecture Notes in Computer Science"

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How Much Should I Double Spend My Bitcoin? Game Theory of Quantum Mining 15th

How Much Should I Double Spend My Bitcoin? Game Theory of Qu...

引用

15th International Conference on Decision and Game Theory for Security

作者： Li, Zhen Liao, Qi Albion Coll Dept Econ & Management Albion MI 49224 USA Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9783031748349;9783031748356

Quantum computing as an inevitable technology can revolutionize many aspects of our society. One potential impact is on cryptocurrency such as Bitcoin, which relies on proof-of-work mining to secure the underlying blockchain protocol. Miners empowered by quantum computers will have superior computational power to win the competition. The quantum advantage jeopardizes the security and trustworthy of cryptocurrency and the transaction validation process by taking over a majority of the network's computing power, known as a 51% attack. Fraudulent Bitcoin transactions in the form of double spending can happen, and the emerging quantum miner could enable double spending and benefit from it. How much double spending is optimal without causing too much "inflation"? What shall be the optimal strategy of the first quantum miner facing the competition from other quantum miners? What are the implications of having one or multiple quantum miners to the security of the Bitcoin network? We conduct a novel game theoretic and economic analysis to address these questions. Simulation illustrates that quantum miners would have to collude to gain from double spending in a quantum competitive environment. The distribution of cryptocurrency between quantum miners and classical miners and how cost-effective classical miners are can affect the profitability and the sustainability of double spending as well as the collusion of quantum miners. Intensified quantum competition will decrease the chance of collusion and eventually make the Bitcoin network secure again. The critical point of quantum popularity that will eliminate double spending is found.

关键词： Game Theory Quantum Computing Cryptocurrency Bitcoin Quantum Mining Double Spending Collusion Cybersecurity Economics

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Multi-agent Based Simulation for Investigating Centralized Charging Strategies and Their Impact on Electric Vehicle Home Charging Ecosystem 4th

Multi-agent Based Simulation for Investigating Centralized C...

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4th Energy Informatics Academy Conference

作者： Christensen, Kristoffer Jorgensen, Bo Norregaard Ma, Zheng Grace Univ Southern Denmark Maersk McKinney Moeller Inst SDU Ctr Energy Informat Fac Engn Odense Denmark

ISBN: (纸本)9783031747373;9783031747380

This paper addresses the critical integration of electric vehicles (EVs) into the electricity grid, which is essential for achieving carbon neutrality by 2050. The rapid increase in EV adoption poses significant challenges to the existing grid infrastructure, particularly in managing the increasing electricity demand and mitigating the risk of grid overloads. Centralized EV charging strategies are investigated due to their potential to optimize grid stability and efficiency, compared to decentralized approaches that may exacerbate grid stress. Utilizing a multi-agent based simulation model, the study provides a realistic representation of the electric vehicle home charging ecosystem in a case study of Strib, Denmark. The findings show that the Earliest-deadline-first and Round Robin perform best with 100% EV adoption in terms of EV user satisfaction. The simulation considers a realistic adoption curve, EV charging strategies, EV models, and driving patterns to capture the full ecosystem dynamics over a long-term period with high resolution (hourly). Additionally, the study offers detailed load profiles for future distribution grids, demonstrating how centralized charging strategies can efficiently manage grid loads and prevent overloads.

关键词： multi-agent based simulation multi-agent systems agent-based modeling electric vehicle charging strategies charging algorithms

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Robust Document Presentation Attack Detection via Diffusion Models and Knowledge Distillation 7th

Robust Document Presentation Attack Detection via Diffusion ...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Li, Bokang Chen, Changsheng Shenzhen Univ Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Online document presentation attack detection (DPAD) in the wild is now advancing towards generalization and robustness. This paper proposes a teacher-student framework with a robust student network capable of handling high-quality and low-quality recaptured images in real-world scenarios when the training set is limited to high-quality data. Our framework addresses the challenge by utilizing diffusion-based simulation methods and knowledge distillation between branch networks. Firstly, we employ the low-quality distortions simulation (LDS) module to synthetic degrade images corresponding to high-quality images, serving as input data for the student network. Then, cross-quality distillation (CQD) is applied at the feature layer before the classification head to constrain the student network to learn rich pre-training highquality features from the teacher network. Combining the LDS and CQD modules, the student network unifies the forensic features of recaptured images with various qualities and can robustly detect them in practical settings. Experimental results demonstrate the effectiveness of our proposed detection method on the DLC2021, RSCID (N), and RSCID (L) datasets, outperforming six large-scale detection backbones when using only ResNet18 as the student network.

关键词： Document presentation attack detection Diffusion model Knowledge distillation

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Enhancing Multi-modal Contrastive Learning via Optimal Transport-Based Consistent Modality Alignment 7th

Enhancing Multi-modal Contrastive Learning via Optimal Trans...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Zhu, Sidan Luo, Dixin Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Minist Educ Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Multi-modal contrastive learning has gained significant attention in recent years due to the rapid growth of multi-modal data and the increasing application demands in practice, e.g., multi-modal pre-training, retrieval, and classification. Most existing multi-modal representation learning methods require well-aligned multi-modal data (e.g., image-text pairs). This setting, however, limits their applications because real-world multi-modal data are often partially-aligned, consisting of a small piece of well-aligned data and a massive amount of unaligned ones. In this study, we propose a novel optimal transport-based method to enhance multi-modal contrastive learning given partially-aligned multi-modal data, which provides an effective strategy to leverage the information hidden in the unaligned multi-modal data. The proposed method imposes an optimal transport (OT) regularizer in the multi-modal contrastive learning framework, aligning the latent representations of different modalities with consistency guarantees. We implement the OT regularizer in two ways, based on a consistency-regularized loop of pairwise Wasserstein distances and a Wasserstein barycenter problem, respectively. We analyze the rationality of our OT regularizer and compare its two implementations in-depth. Experiments show that combining our OT regularizer with state-of-the-art contrastive learning methods leads to better performance in the generalized zero-shot cross-modal retrieval and multi-modal classification tasks.

关键词： Multi-modal Contrastive Learning Optimal Transport Modality Alignment Generalized Zero-shot Learning

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AIREG: Enhanced Educational Recommender System with Large Language Models and Knowledge Graphs

AIREG: Enhanced Educational Recommender System with Large La...

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21st International Conference on The Semantic Web (ESWC)

作者： Fathi, Fatemeh Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9783031789540;9783031789557

In the nowadays modern digital era, the overwhelming amount of available online data has established challenges for individuals seeking personalized educational and career pathways with relevant skill dependencies, especially when surfing e-learning and online recruitment platforms. This challenge emphasizes the need for novel advancements in knowledge-enhanced Recommender Systems, offering more personalized, accurate, and timely recommendations. Recently, the rapid development of Large Language Models (LLMs) with their broad knowledge and complex reasoning skills, has significantly enhanced the ability of these systems to offer precise and knowledge-based suggestions. It highlights their potential to enrich these systems using their vast amount of knowledge and sophisticated reasoning capabilities, to leverage them as an alternative to structured knowledge bases like knowledge graphs (KGs). However, LLMs have still limitations for knowledge-based content generation, especially when it's a domain-specific case. To address this issue, researchers propose to enhance the system with explicit factual knowledge from KGs. This research aims to explore advanced technological developments in knowledge-enhanced conversational Recommender Systems to propose a novel system, named AIREG for the educational and career development sectors.

关键词： Recommender System Large Language Model Knowledge Graph Knowledge Extraction

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Spatial-Frequency Dual-Stream Reconstruction for Deepfake Detection 7th

Spatial-Frequency Dual-Stream Reconstruction for Deepfake De...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Peng, Chunlei Chen, Tao Liu, Decheng Zheng, Yu Wang, Nannan Xidian Univ State Key Lab Integrated Serv Networks Sch Cyber Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

ISBN: (纸本)9789819787944;9789819787951

The widespread usage of Deepfake technology poses a significant threat to societal security, making the detection of Deepfakes a critical area of research. In recent years, forgery detection methods based on reconstruction errors have garnered widespread attention due to their excellent performance and generalization capabilities. However, those methods often focus on spatial reconstruction errors while neglecting the potential utility of frequency-based reconstruction errors. In this paper, we propose a novel deepfake detection framework based on Spatial-Frequency Dual-stream Reconstruction (SFDR). Specifically, our approach to forgery detection utilizes both frequency reconstruction error and spatial reconstruction error to provide complementary information that enhances the detection process. In addition, during the reconstruction, we ensure the consistency of frequency content between the original genuine images and their reconstructed versions. Finally, to mitigate the adverse impact of reconstruction tasks on the performance of forgery detection, we have refined the reconstruction loss to minimize the discrepancy between the original genuine images and their reconstructed counterparts;while simultaneously maximizing the difference between manipulated images and their reconstructions. Experimental results on multiple challenging forged datasets evaluation show that our method achieves superior performance in detection and generalization ability.

关键词： Deepfake Detection Reconstruction Learning Generalization

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Balance-Based ZKP Protocols for Pencil-and-Paper Puzzles 27th

Balance-Based ZKP Protocols for Pencil-and-Paper Puzzles

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27th International Conference on Information Security

作者： Kaneko, Shohei Lafourcade, Pascal Mallordy, Lola-Baie Miyahara, Daiki Puys, Maxime Sakiyama, Kazuo Univ Electrocommun Tokyo Japan Univ Clermont Auvergne LIMOS Clermont Auvergne INP CNRSMines St Etienne F-63000 Clermont Ferrand France Univ Clermont Auvergne LIMOS CNRS UMR 6158 Aubiere France Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9783031757563;9783031757570

In this paper, we propose zero-knowledge proof (ZKP) protocols using physical objects for four pencil-and-paper puzzles: the well-known Sudoku as well as Makaro, Futoshiki, and Kakuro. That is, our protocols allow a prover to convince a verifier that the prover knows a solution to a puzzle without relying on the use of computers. While previous physical ZKP protocols for puzzles have mainly relied on decks of cards, our research introduces a novel approach utilizing a balance scale and coins to design balance-based ZKP protocols;moreover we show its flexibility by adapting it to the four different puzzles. We compare the number of coins and operations in our protocols with the existing card-based protocols and show that, for certain puzzles, our balance-based protocol outperforms the card-based method. Finally, we prove that our protocols achieve perfect completeness, perfect soundness and are perfectly zero-knowledge.

关键词： Card-based cryptography Zero-knowledge proof Balance scale Pencil-and-paper puzzle Sudoku

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Using Diffusion Models for Data Augmentation on Limited Rodent OCT Datasets 25th

Using Diffusion Models for Data Augmentation on Limited Rode...

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25th International Conference on Intelligent Data Engineering and Automated Learning

作者： Garcia-Torres, Fernando del Amor, Roefo Morales-Martinez, Sandra Barroso, Alvaro Kemper, Bjoern Schnekeriburger, Jurgen Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano Valencia Spain Univ Munster Biomed Technol Ctr Med Fac Mendelstr 17 D-48149 Munster Germany

ISBN: (纸本)9783031777301;9783031777318

Optical coherence tomography (OCT) has transformed ophthalmology by enabling accurate diagnosis of retinal diseases. OCT in rodents is used in research studies of ophthalmic diseases and treatment evaluation. However, the scarcity of data limits the effectiveness of deep learning models for this purpose. This study explores the use of synthetic data generation to improve deep learning models for OCT image analysis from rodent retinas. To address this, the research leverages advanced class-conditional generative models to create high-quality synthetic images of ex-vivo rodent retinas embedded in two different mediums (solid resin and liquid media) with a reduced initial database. The conditional Denoising Diffusion Probabilistic Model (DDPM) proved to be the best model, as it produces more realistic images that closely mimic the characteristics of real rodent retinal scans. By augmenting the training datasets with these synthetic images, the performance of deep learning models in segmenting retinal layers improved by 7%. This approach reduces reliance on scarce biological samples and animal testing, promoting ethical research practices while improving the accuracy and efficiency of AI-driven medical diagnostics. The integration of synthetic data generation represents a significant advance in medical imaging, offering a scalable solution to data scarcity, generating reliable samples in two mediums and promoting the development of more robust and accurate diagnostic tools.

关键词： Synthetic data generation Deep learning Optical coherence tomography Image analysis Retinal diseases Denoising Diffusion Probabilistic Models Medical imaging

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Augmenting Interpolation-Based Model Checking with Auxiliary Invariants 30th

Augmenting Interpolation-Based Model Checking with Auxiliary...

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30th International Symposium on Model Checking Software

作者： Beyer, Dirk Chien, Po-Chun Lee, Nian-Ze Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9783031661488;9783031661495

Software model checking is a challenging problem, and generating relevant invariants is a key factor in proving the safety properties of a program. Program invariants can be obtained by various approaches, including lightweight procedures based on data-flow analysis and intensive techniques using Craig interpolation. Although data-flow analysis runs efficiently, it often produces invariants that are too weak to prove the properties. By contrast, interpolation-based approaches build strong invariants from interpolants, but they might not scale well due to expensive interpolation procedures. Invariants can also be injected into model-checking algorithms to assist the analysis. Invariant injection has been studied for many well-known approaches, including k-induction, predicate abstraction, and symbolic execution. We propose an augmented interpolation-based verification algorithm that injects external invariants into interpolation-based model checking (McMillan, 2003), a hardware model-checking algorithm recently adopted for software verification. The auxiliary invariants help prune unreachable states in Craig interpolants and confine the analysis to the reachable parts of a program. We implemented the proposed technique in the verification framework CPACHECKER and evaluated it against mature SMT-based methods in CPACHECKER as well as other state-of-the-art software verifiers. We found that injecting invariants reduces the number of interpolation queries needed to prove safety properties and improves the run-time efficiency. Consequently, the proposed invariant-injection approach verified difficult tasks that none of its plain version (i.e., without invariants), the invariant generator, or any compared tools could solve.

关键词： Software model checking Program invariants Invariant injection Craig interpolation Data-flow analysis SMT SAT

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Registered FE Beyond Predicates: (Attribute-Based) Linear Functions and More 30th

Registered FE Beyond Predicates: (Attribute-Based) Linear Fu...

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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Datta, Pratish Pal, Tapas Yamada, Shota NTT Res Sunnyvale CA 94085 USA Karlsruhe Inst Technol KASTEL SRL D-76131 Karlsruhe Germany Natl Inst Adv Ind Sci & Technol Tokyo 1350064 Japan

ISBN: (纸本)9789819608744;9789819608751

This paper introduces the first registered functional encryption RFE scheme tailored for linear functions. Distinctly different from classical functional encryption (FE), RFE addresses the key-escrow issue and negates the master key exfiltration attack. Instead of relying on a centralized trusted authority, it introduces a "key curator" - a fully transparent entity that does not retain secrets. In an RFE framework, users independently generate secret keys and subsequently register their respective public keys, along with their authorized functions, with the key curator. This curator consolidates public keys from various users into a unified, concise master public key. For decryption, users occasionally secure helper decryption keys from the key curator, which they use in conjunction way with their private keys. It is imperative that the aggregate public key, helper decryption keys, ciphertexts, and the times for encryption/decryption are polylogarithmic in the number of registered users. All existing RFE designs were confined to predicates where given the correct credentials a user can retrieve the entire payload from a ciphertext or gain no information about it otherwise. Contrarily, our RFE scheme facilitates the computation of linear functions on encrypted content and extraction of only the computation results. Recognizing potential leaks from linear functions, we further enhance our RFE by incorporating an attribute-based access control mechanism. The outcome is the first registered attribute-based linear FE (RABIPFE), which supports access policies depicted as linear secret sharing schemes LSSS. Our proposed schemes are realized in the common reference string (CRS) model as introduced by Hohenberger et al.[EUROCRYPT 2023], employ simple tools and black-box methods. Specifically, our constructions operate in asymmetric prime-order bilinear group setting and are proven secure in the generic bilinear group model. Aligning with all pre-existing black-box RFE design

关键词： Ciphertext

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