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

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Post-quantum Multi-client Conjunctive Searchable Symmetric Encryption from Isogenies 14th

Post-quantum Multi-client Conjunctive Searchable Symmetric E...

引用

14th International Conference on Security, Privacy and Applied Cryptographic Engineering (SPACE)

作者： Patranabis, Sikhar IBM Res Bangalore Karnataka India

ISBN: (纸本)9783031804076;9783031804083

Multi-client searchable symmetric encryption (SSE) allows multiple third-party clients to execute fast encrypted search queries over a symmetrically encrypted database created by a data owner and held by an (untrusted) data server. Although SSE is ostensibly a symmetric-key cryptoprimitive, existing multi-client SSE schemes that support conjunctive and general Boolean queries rely crucially on the classical hardness of the discrete log problem over cyclic groups, and are completely broken by quantum attacks. This leaves open the question of designing multiclient conjunctive (and more expressive) SSE schemes from plausibly quantum-safe assumptions. In this paper, we present the first plausibly quantum-safe multi-client SSE scheme supporting conjunctive keyword queries while relying on the hardness of certain isogeny-based assumptions (such as CSIDH and CSI-FiSh) that can be modeled using cryptographic group actions. As a core technical contribution, we present a novel adaptation of the widely studied but quantum-broken Oblivious Cross-Tags (OXT) protocol (Cash et al., Crypto 2013) to the setting of cryptographic group actions. This scheme, which we call GXT, supports conjunctive keyword queries in the single-client setting. We then present MC-GXT - an extension of GXT to the multi-client setting. Our constructions match the asymptotic efficiency guarantees of the original OXT scheme in terms of storage requirements and conjunctive query complexity, while additionally providing data and query privacy guarantees based on well-studied and plausibly quantum-safe isogeny-based hardness assumptions.

关键词： Searchable Symmetric Encryption Multi-client SSE Post-quantum Security Isogeny-based Cryptography Group Actions

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Generic Differential Key Recovery Attacks and Beyond 30th

Generic Differential Key Recovery Attacks and Beyond

引用

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

作者： Song, Ling Liu, Huimin Yang, Qianqian Chen, Yincen Hu, Lei Weng, Jian Jinan Univ Coll Cyber Secur Guangzhou Peoples R China Chinese Acad Sci Inst Informat Engn Key Lab Cyberspace Secur Def Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9789819609406;9789819609413

At Asiacrypt 2022, a holistic key guessing strategy was proposed to yield the most efficient key recovery for the rectangle attack. Recently, at Crypto 2023, a new cryptanalysis technique-the differential meet-in-the-middle (MITM) attack-was introduced. Inspired by these two previous works, we present three generic key recovery attacks in this paper. First, we extend the holistic key guessing strategy from the rectangle to the differential attack, proposing the generic classical differential attack (GCDA). Next, we combine the holistic key guessing strategy with the differential MITM attack, resulting in the generalized differential MITM attack (GDMA). Finally, we apply the MITM technique to the rectangle attack, creating the generic rectangle MITM attack (GRMA). In terms of applications, we improve 12/13-round attacks on AES-256. For 12-round AES-256, by using the GDMA, we reduce the time complexity by a factor of 2(62);by employing the GCDA, we reduce both the time and memory complexities by factors of 2(61) and 2(56), respectively. For 13-round AES-256, we present a new differential attack with data and time complexities of 2(89) and 2(240), where the data complexity is 2(37) times lower than previously published results. These are currently the best attacks on AES-256 using only two related keys. For KATAN-32, we increase the number of rounds covered by the differential attack from 115 to 151 in the single-key setting using the basic differential MITM attack (BDMA) and GDMA. Furthermore, we achieve the first 38-round rectangle attack on SKINNYe-64-256 v2 by using the GRMA.

关键词： Differential cryptanalysis Rectangle attack Meet-in-the-middle Key recovery AES KATAN SKINNYe

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Trajectory-based Calibration for Optical See-Through Head-Mounted Displays Without Alignment 7th

Trajectory-based Calibration for Optical See-Through Head-Mo...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Wang, Yongqi Zhao, Shaohua Chen, Wei Shi, ZhongChen Xie, Liang Yan, Ye Yin, ErWei Tianjin Univ Sch Mech Engn Tianjin Peoples R China Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin Peoples R China Natl Def Technol Innovat Res Inst Beijing Peoples R China Intelligent Game & Decis Lab Beijing Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Optical see-through head-mounted display (OST-HMD) calibration is crucial for virtual and real content alignment of augmented reality applications. Most conventional OST-HMD calibration methods heavily rely on manual alignment of virtual and real content to compute the transformation parameters of relevant coordinates. However, manual alignment is cumbersome and time-consuming, which limits the wide application of these methods. To address this issue, we propose a new alignment-free calibration method based on trajectory generation for robust OST-HMD calibration. There are three main steps of the proposed method: 1) Obtaining calibration image data from different coordinate spaces following predefined trajectories;2) Calculate the transformation parameter from the real world to tracking camera and observation camera as well as the transformation parameter from virtual content to observation camera;3) Derive the OST-HMD calibration parameters from above transformation parameters. The advantages of our method are: 1) Combined with the trajectories calibration mechanism, the proposed method can get more robust results than traditional approaches;2) due to the precise geometry derivation from 3D-3D correspondences, it can naturally avoid manual alignment and obtain accurate calibration results without the requirement of any internal parameters of the virtual camera or the display parameters of OST-HMD devices. Extensive experiments show that the proposed method can achieve better calibration parameters than other methods on both accuracy and robustness.

关键词： Augmented Reality Optical See-Through Head-Mounted Display Calibration

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STEVE: A Rational Verification Tool for Stackelberg Security Games 19th

STEVE: A Rational Verification Tool for Stackelberg Security...

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19th International Conference on Integrated Formal Methods

作者： Phetmanee, Surasak Sevegnani, Michele Andrei, Oana Univ Glasgow Sch Comp Sci Glasgow G12 8RZ Lanark Scotland

ISBN: (纸本)9783031765537;9783031765544

We present STEVE, a prototype tool modelling Stackelberg Security Games (SSGs) and employing rational verification based on bespoke Stackelberg equilibrium computation. STEVE automatically extracts technical details from public vulnerability databases, transforming these into Attack Defence Trees and then into SSG models. By using the temporal logic rPATL, the tool enables the synthesis of optimal defence strategies through Stackelberg equilibrium analysis, which is implemented as a PRISM-games extension. Preliminary results demonstrate STEVE's ability to model and counteract cyber threats, reducing potential damages and financial losses.

关键词： Rational Verification Security Games Temporal Logic

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Affine Disjunctive Invariant Generation with Farkas' Lemma 26th

Affine Disjunctive Invariant Generation with Farkas' Lemma

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26th International Conference on Verification Model Checking and Abstract Interpretation

作者： Ke, Jingyu Fu, Hongfei Liu, Hongming Sun, Zhouyue Chen, Liqian Li, Guoqiang Shanghai Jiao Tong Univ Shanghai Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9783031826993;9783031827006

In the verification of loop programs, disjunctive invariants are essential to capture complex loop dynamics such as phase and mode changes. In this work, we develop a novel approach for the automated generation of affine disjunctive invariants for affine while loops via Farkas' Lemma, a fundamental theorem on linear inequalities. Our main contributions are two-fold. First, we combine Farkas' Lemma with a succinct control flow transformation to derive disjunctive invariants from the conditional branches in the loop. Second, we propose an invariant propagation technique that minimizes the invariant computation effort by propagating previously solved invariants to yet unsolved locations as much as possible. Furthermore, we resolve the infeasibility checking in the application of Farkas' Lemma which has not been addressed previously, and extend our approach to nested loops via loop summary. Experimental evaluation over more than 100 affine while loops (mostly from SV-COMP 2023) demonstrates that our approach is promising to generate tight linear invariants over affine programs.

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Datatypes for Lists and Maps in RDF Literals

Datatypes for Lists and Maps in RDF Literals

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21st International Conference on The Semantic Web (ESWC)

作者： Hartig, Olaf Williams, Gregory Todd Schmidt, Michael Lassila, Ora Enriquez, Carlos Manuel Lopez Thompson, Bryan Amazon Web Serv Amazon Neptune Team Seattle WA 98109 USA Linkoping Univ Linkoping Sweden

ISBN: (纸本)9783031789519;9783031789526

We present an approach to represent composite values (lists and maps, in particular) as literals in RDF data, and to extend SPARQL with features related to such literals. These extensions include an aggregation function to produce these composite values, functions to operate on these composite values in expressions, and a new operator to unfold such composite values into their individual components. As resources related to the approach, we provide two complete open source implementations, a formal specification, and a comprehensive test suite.

关键词： Composite Datatypes SPARQL Extension JSON in RDF

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Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental Learning 7th

Adaptive Margin Global Classifier for Exemplar-Free Class-In...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Yao, Zhongren Chang, Xiaobin Sun Yat Sen Univ Sch Artificial Intelligence Guangzhou Peoples R China

ISBN: (纸本)9789819784868;9789819784875

Exemplar-free class-incremental learning (EFCIL) presents a significant challenge as the old class samples are absent for new task learning. Due to the severe imbalance between old and new class samples, the learned classifiers can be easily biased toward the new ones. Moreover, continually updating the feature extractor under EFCIL can compromise the discriminative power of old class features, e.g., leading to less compact and more overlapping distributions across classes. Existing methods mainly focus on handling biased classifier learning. In this work, both cases are considered using the proposed method. Specifically, we first introduce a Distribution-Based Global Classifier (DBGC) to avoid bias factors in existing methods, such as data imbalance and sampling. More importantly, the compromised distributions of old classes are simulated via a simple operation, variance enlarging (VE). Incorporating VE based on DBGC results in a novel classification loss for EFCIL. This loss is proven equivalent to an Adaptive Margin Softmax Cross Entropy (AMarX). The proposed method is thus called Adaptive Margin Global Classifier (AMGC). AMGC is simple yet effective. Extensive experiments show that AMGC achieves superior image classification results on its own under a challenging EFCIL setting.

关键词： Class-incremental learning Exemplar-free Marginal loss

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On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs 17th

On the Design of Scalable Outlier Detection Methods Using Ap...

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17th International Conference on Similarity Search and Applications

作者： Okkels, Camilla Birch Aumuller, Martin Zimek, Arthur IT Univ Copenhagen Copenhagen Denmark Univ Southern Denmark Odense Denmark

ISBN: (纸本)9783031758225;9783031758232

Efficient and reliable methods for distinguishing outliers in data remain crucial for data analysis. Although supervised methods based on neural networks have gained recent traction, unsupervised methods such as the kNN outlier method and local outlier factor (LOF) remain state-of-the-art solutions according to different standardized benchmarks. Unfortunately, exact outlier detection through nearest neighbor search queries provides a scalability bottleneck for the high-dimensional, big datasets that are routinely analyzed in data science applications. This paper explores benefits and limitations of using approximate nearest neighbor search via Hierarchical Navigable Small World graphs (HNSW) to overcome this scalability barrier. We evaluate direct implementations that compute the kNN and LOF score from approximate neighborhoods and show the robustness of the outlier detection even in settings where the approximation is far away from the exact neighborhoods. Furthermore, we design white-box methods that compute the outlier scores directly from the underlying graph. These methods show much more variability in the quality of the outlier scores and open new ground for the development of task-aware tools based on approximate nearest neighbor search techniques.

关键词： Nearest neighbor search

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Non-reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs 4th

Non-reference Quality Assessment for Medical Imaging: Applic...

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4th Workshop on Deep Generative Models for Medical Image Computing and computer Assisted Intervention

作者： Risager, Karl Van Eeden Gholamalizadeh, Torkan Ghazi, Mostafa Mehdipour Univ Copenhagen Dept Comp Sci Pioneer Ctr AI Copenhagen Denmark 3Shape AS Res & Dev Copenhagen Denmark

ISBN: (纸本)9783031727436;9783031727443

Generating high-quality synthetic data is crucial for addressing challenges in medical imaging, such as domain adaptation, data scarcity, and privacy concerns. Existing image quality metrics often rely on reference images, are tailored for group comparisons, or are intended for 2D natural images, limiting their efficacy in complex domains like medical imaging. This study introduces a novel deep learning-based non-reference approach to assess brain MRI quality by training a 3D ResNet. The network is designed to estimate quality across six distinct artifacts commonly encountered in MRI scans. Additionally, a diffusion model is trained on diverse datasets to generate synthetic 3D images of high fidelity. The approach leverages several datasets for training and comprehensive quality assessment, benchmarking against state-of-the-art metrics for real and synthetic images. Results demonstrate superior performance in accurately estimating distortions and reflecting image quality from multiple perspectives. Notably, the method operates without reference images, indicating its applicability for evaluating deep generative models. Besides, the quality scores in the [0, 1] range provide an intuitive assessment of image quality across heterogeneous datasets. Evaluation of generated images offers detailed insights into specific artifacts, guiding strategies for improving generative models to produce high-quality synthetic images. This study presents the first comprehensive method for assessing the quality of real and synthetic 3D medical images in MRI contexts without reliance on reference images.

关键词： Deep learning medical imaging quality assessment brain MRI generative models

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Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images 5th

Interactive Segmentation Model for Placenta Segmentation fro...

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5th International Workshop on Advances in Simplifying Medical Ultrasound

作者： Li, Hao Oguz, Baris Arenas, Gabriel Yao, Xing Wang, Jiacheng Pouch, Alison Byram, Brett Schwartz, Nadav Oguz, Ipek Vanderbilt Univ Nashville TN 37235 USA Univ Penn Philadelphia PA USA

ISBN: (纸本)9783031736469;9783031736476

Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is expensive and time consuming. Automated segmentation algorithms can often successfully segment the placenta, but these methods may not consistently produce robust segmentations suitable for practical use. Recently, inspired by the Segment Anything Model (SAM), deep learning-based interactive segmentation models have been widely applied in the medical imaging domain. These models produce a segmentation from visual prompts provided to indicate the target region, which may offer a feasible solution for practical use. However, none of these models are specifically designed for interactively segmenting 3D ultrasound images, which remain challenging due to the inherent noise of this modality. In this paper, we evaluate publicly available state-of-the-art 3D interactive segmentation models in contrast to a human-in-the-loop approach for the placenta segmentation task. The Dice score, normalized surface Dice, averaged symmetric surface distance, and 95-percent Hausdorff distance are used as evaluation metrics. We consider a Dice score of 0.95 a successful segmentation. Our results indicate that the human-in-the-loop segmentation model reaches this standard. Moreover, we assess the efficiency of the human-in-the-loop model as a function of the amount of prompts. Our results demonstrate that the human-in-the-loop model is both effective and efficient for interactive placenta segmentation. The code is available at https://***/MedICL-VU/PRISM-placenta.

关键词： Interactive segmentation Scribbles Deep learning Placenta segmentation 3D Ultrasound (3DUS) image

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