Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense methods often suffer from (1) high computational overhead and (2) poor generalization ability across diverse attack types. To bridge these gaps, we propose a novel yet efficient teacher-student framework, namely Multimodal Robust Prompt Distillation (MRPD) for distilling robust 3D point cloud model. It learns lightweight prompts by aligning student point cloud model's features with robust embeddings from three distinct teachers: a vision model processing depth projections, a high-performance 3D model, and a text encoder. To ensure a reliable knowledge transfer, this distillation is guided by a confidence-gated mechanism which dynamically balances the contribution of all input modalities. Notably, since the distillation is all during the training stage, there is no additional computational cost at inference. Extensive experiments demonstrate that MRPD substantially outperforms state-of-the-art defense methods against a wide range of white-box and black-box attacks, while even achieving better performance on clean data. Our work presents a new, practical paradigm for building robust 3D vision systems by efficiently harnessing multimodal knowledge.

ML-DSA (formerly CRYSTALS-Dilithium), NIST’s primary post-quantum signature standard, is increasingly deployed along with the post-quantum transitions. Yet when the implementations of ML-DSA are deployed in practice, their physical security remains underexplored. In this work, we reveal a new attack surface against ML-DSA by exploiting the leakages from both rejected signing trials and the final accepted signing trial. We present, to the best of our knowledge, the first side-channel attack that simultaneously leverages leakage from both trials without relying on clone devices. Unlike traditional Secret-based Template Attacks, which require profiling the leakage of the sensitive intermediates on a clone device, our PTA (Public-based Template Attack) builds leakage templates solely from publicly available data on the target device itself. With challenge c known, we then perform CPA on the sensitive intermediates using traces from both rejected and accepted signing trials, quadrupling (on average) exploitable leakage per signing request for ML-DSA-44. The experimental results on power traces from an ARM Cortex-M4 board show that challenges c are fully recovered with only 96 traces, and then the key recovery succeeds in around 300 traces — a fact of 10x fewer than prior art. We highlight that our attack can be applied across all three ML-DSA variants with different security levels. Moreover, our attack works straightforwardly in the hedged (non-deterministic) mode of ML-DSA, demonstrating that the hedging offers no SCA protection in this scenario.

Abstract Software-Defined Networking (SDN), as a new network architecture, has brought convenience, but also suffered from the threat of Distributed Denial of Service (DDoS) attack. However, most existing DDoS attack detection schemes for SDN employ only a single detection method, leading to imbalances in detection speed, system overhead, and detection accuracy. Even though a few schemes improve detection efficiency and accuracy through two-stage detection, they still suffer from low system flexibility and do not support dynamic threshold adjustment. In order to resolve these issues, we propose an adaptive two-stage DDoS attack detection scheme with Dynamic Threshold Adjustment (ATS-DTA for short), which contains three sub-modules. More specifically, by dividing DDoS attack detection into two modules: a conditional entropy-based network traffic anomaly detection phase and a DDoS attack detection phase based on machine learning methods. Additionally, an adaptive threshold adjustment module is introduced to improve the system’s flexibility. Finally, the experimental results show that our scheme, compared to related schemes, not only significantly improves detection accuracy and speed but also supports flexible and dynamic threshold adjustment. Specifically, our method achieves an average accuracy improvement of 1.91% and a precision increase of 1.23% over baseline methods, underscoring its effectiveness in adapting to complex and evolving network environments. These advantages illustrate that our ATS-DTA scheme provides a more balanced, efficient, and reliable solution for DDoS detection in dynamic network scenarios.

Abstract The proliferation of spatial-textual data applications has created significant challenges in securely managing such data within untrusted cloud environments. Existing encrypted spatial-textual data retrieval schemes primarily focus on static data and overlook the complexities of practical data updates, particularly lacking robustness in managing irrational updates. In this paper, we introduce a novel robust dynamic encrypted spatial-textual data search scheme, called , that enhances existing systems by addressing the challenges of security and robustness in data dynamic settings. This is the first scheme to simultaneously achieve forward security, Type-I $$^-$$ - backward security, and enhanced robustness for boolean range queries on spatial-textual data. We formally define the security model and classify three levels of robustness. Our customized Asymmetric Scalar-Product-Preserving Encryption (ASPE) design incorporates an “update check mechanism” can efficiently mitigate repeated and disruptive update attacks while supporting efficient search and update permission control. Experimental evaluations demonstrate that only maintains 100% precision and recall while showing practical search efficiency, even outperforming the existing static scheme with a similar ASPE-based approach.

NIST has selected Falcon as one of the standardized post-quantum digital signature algorithms, making the security of Falcon against side-channel attacks (SCAs) a critical area of concern. This paper introduces SERP-SCA, a strengthened SCA framework for Falcon, which exploits leakage from floating-point multiplications to recover secret floating-point values, which can subsequently be mapped to the coefficients of the secret key. The framework adopts a divide-and-conquer strategy to target the mantissa, exponent, and sign bit of floating-point values, with specialized optimizations for attacks on the mantissa and exponent. For mantissa attacks, SERP-SCA integrates bit segmentation with a sequential attack strategy to fully exploit the leakage from mantissa multiplication and incorporates an error-correction mechanism to further enhance attack efficiency. For exponent attacks, SERP-SCA leverages Fast Fourier Transform (FFT) properties to narrow the enumeration space, significantly improving time efficiency. We conduct practical attacks on Falcon-512 and Falcon-1024 implementations running on ARM Cortex-M4. Compared to state-of-the-art methods, SERP-SCA achieves an improvement of 22.11% in success rate for Falcon-512 and 18.64% for Falcon-1024, with remarkable time efficiency gains of around 195,369× and 183,175× for mantissa attacks, and 180× and 185× for exponent attacks, respectively.

Quantization-conditioned backdoor attacks, which exploit model quantization states to trigger malicious behavior, pose a hidden threat to deep learning security. However, current studies ignore the feasibility of attacks,i.e., detection visibility, and computational overhead, leading to significant constraints on their practical deployment in real-world adversarial scenarios. To address these limitations, we propose Quantization-conditioned Efficient Stealthy Trojan (QuEST), a novel framework that enhances both the stealth and efficiency of backdoor attacks under quantization constraints. For enhancing stealthiness, we design a stealth-optimized training scheme that benefits from the parametric backdoor injection and trigger scaling augmentation to maintain the consistency of model behavior during attack. In this way, the defender will fail to capture the suspicious behavior differences for detection due to the made efforts in both model-side and data-side. To improve efficiency, we introduce information-guided parameter sharing, which utilizes parameter redundancy analysis and Fisher divergence metrics to identify a minimal amount of quantization-preserved parameters for back-door injection. These parameters are strategically shared between the malicious and benign models, enabling concurrent training and substantially reducing overall training time. Extensive experiments demonstrate that QuEST maintains competitive attack success rates while improving stealth performance by 18.75% and reducing computational costs by 26.66% on average compared to state-of-the-art methods, highlighting QuEST’s potential for more practical adversarial deployments in real-world scenarios. Our code is available here.

Side-channel attack poses a significant threat to the security of electronic devices, particularly IoT/AIoT terminals. By leveraging side-channel leakages, collision attacks can efficiently extract the secret keys from cryptographic devices while requiring considerably less computational effort. In this paper, we investigate side-channel collision attacks against ASCON, a lightweight crypto designed for resource-constrained devices, which has been standardized by the NIST. For the first time, we propose a side-channel key recovery attack against ASCON by identifying the collisions in the linear diffusion layer. Using Pearson correlation coefficient and Euclidean distance for internal collision detections, our attack successfully recovers the secret key with approximately 5,000 power traces from an 8-bit software implementation on an AVR device. To further reduce attack complexity, we introduce a novel metric, Locally-Weighted Sum (LWS), which focuses on the most likely points of leakage, thereby decreasing the number of required power traces for successful attack. Our experiment on the same target demonstrates that the LWS-based collision attack can recover the full secret key with approximately 3,000 power traces, a reduction of 40 percent. Our study indicates that ASCON is susceptible to side-channel collision attacks, and bitslice implementations remain vulnerable to such threats.

Cuckoo hashing serves as a fundamental technique in various privacy-enhancing cryptographic primitives, such as Private Information Retrieval, Symmetric Searchable Encryption, owing to its excellent performance. However, achieving space-efficient Cuckoo hashing that maintains fast insertion and query operations, while facilitating its applications with customized design, remains highly challenging. In this work, we propose a multi-segment permutation-based Cuckoo hashing (MS-PCH) that can be efficiently parallelized on multi-threaded platforms, followed by a strategy for further parallelization over the hashing within single segments (MH-MS-PCH). To demonstrate its practical utility, we then investigate its application on encrypted data search by constructing full-fledged Public Key (Authenticated) Encryption with Keyword Search schemes (PCH(-MD)-PEKS and PCH(-MD)-PAEKS). We evaluate the performance of these schemes on a public dataset, showing that, with 16 segments and 4 hash functions, both PCH(-MD)-PEKS and PCH(-MD)-PAEKS outperforms the plain PEKS and PAEKS across index generation, query, and update. Notably, our optimized Cuckoo hashing achieves up to a 10 times improvement over plain cuckoo hashing, and our enhanced P(A)EKS schemes demonstrate approximately a 6 times improvement in index generation efficiency and a 335 times acceleration in query processing.

Falcon has been selected by the National Institute of Standards and Technology (NIST) as one of the standardized post-quantum digital signature algorithms. Unlike other candidate algorithms, Falcon relies heavily on floating-point operations, which are known to be vulnerable to side-channel attacks (SCAs). Although recent research proposes masking schemes for basic floating-point operations, secure conversion between integer and floating-point representations remains unaddressed. These conversions operate directly on the private key or other sensitive variables and therefore constitute a potential attack surface. This paper proposes new masking gadgets that securely perform conversions between integers and floating-point representations, addressing this previously unprotected surface. As part of the design, we optimize existing normalization and floating-point composition components to support the conversion process. We formally prove that our gadgets satisfy$t$-Non-Interference$(t-\text{NI})$or$t$-Strong Non-Interference ($t$-SNI) in the probing model and evaluate their side-channel resistance using Test Vector Leakage Assessment (TVLA) on an Arm Cortex-M4 processor. We also assess performance on an Intel Core CPU, where the optimized normalization and floating-point composition components demonstrate improvements of approximately 47.6 % and 10.8 %, respectively, over prior work.

Adversarial robustness distillation (ARD) aims to transfer both performance and robustness from teacher model to lightweight student model, enabling resilient performance on resource-constrained scenarios. Though existing ARD approaches enhance student model's robustness, the inevitable by-product leads to the degraded performance on clean examples. We summarize the causes of this problem inherent in existing methods with dual-teacher framework as: 1. The divergent optimization objectives of dual-teacher models, i.e., the clean and robust teachers, impede effective knowledge transfer to the student model, and 2. The iteratively generated adversarial examples during training lead to performance deterioration of the robust teacher model. To address these challenges, we propose a novel Cyclic Iterative ARD (CIARD) method with two key innovations: a. A multi-teacher framework with contrastive push-loss alignment to resolve conflicts in dual-teacher optimization objectives, and b. Continuous adversarial retraining to maintain dynamic teacher robustness against performance degradation from the varying adversarial examples. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CIARD achieves remarkable performance with an average 3.53 improvement in adversarial defense rates across various attack scenarios and a 5.87 increase in clean sample accuracy, establishing a new benchmark for balancing model robustness and generalization. Our code is available at https://github.com/eminentgu/CIARD

Deep neural networks for 3D point clouds have been demonstrated to be vulnerable to adversarial examples. Previous 3D adversarial attack methods often exploit certain information about the target models, such as model parameters or outputs, to generate adversarial point clouds. However, in realistic scenarios, it is challenging to obtain any information about the target models under conditions of absolute security. Therefore, we focus on transfer-based attacks, where generating adversarial point clouds does not require any information about the target models. Based on our observation that the critical features used for point cloud classification are consistent across different DNN architectures, we propose CFG, a novel transfer-based black-box attack method that improves the transferability of adversarial point clouds via the proposed Critical Feature Guidance. Specifically, our method regularizes the search of adversarial point clouds by computing the importance of the extracted features, prioritizing the corruption of critical features that are likely to be adopted by diverse architectures. Further, we explicitly constrain the maximum deviation extent of the generated adversarial point clouds in the loss function to ensure their imperceptibility. Extensive experiments conducted on the ModelNet40 and ScanObjectNN benchmark datasets demonstrate that the proposed CFG outperforms the state-of-the-art attack methods by a large margin.

The post-quantum cryptography standard, released in August 2024, faces significant threats from side-channel attacks (SCAs). While non-profiled attacks demand extensive traces and time, profiled attacks, though more effective, require access to identical devices. This paper proposes a hybrid SCA on Dilithium, leveraging the strengths of both approaches. Our method profiles a template using leakage from operations with known variables, then applies it to target sensitive variables. Specifically, Convolutional Neural Networks (CNNs) are employed to model leakages from Inverse Number Theoretic Transform (INTT) operations. This model is subsequently used to recover private keys by targeting INTT operations involving c. Implemented on an ARM Cortex-M4 platform, our attack requires only 10/27/18 power traces to recover keys for Dilithium2/3/5, respectively, significantly reducing the number of traces compared to prior non-profiled attacks requiring hundreds.

This paper studies the high-performance implementation of Classic McEliece Key Encapsulation Mechanism (KEM), one of the candidates in the fourth round NIST Post-Quantum Cryptography competition. We focus on batch processing scenarios and improve the performance of key components in Classic McEliece through loop unrolling optimization. The study reveals that while loop unrolling is effective in improving performance, it may lead to performance degradation during the key encapsulation phase under high task loads. To address this issue, we introduce inline expansion optimization. We evaluate our approach on NVIDIA GeForce GTX 1660S and RTX 3090 platforms. The results show that loop unrolling optimization in the key encapsulation phase reduces latency by up to 54.09% (resp. 74.53%) at low task loads, while inline expansion optimization increases throughput by up to 16.17% (resp. 30.31%) at high task loads. In the key decapsulation phase, loop unrolling optimization reduces latency by up to 96.97% (resp. 96.54%) at low task loads and increases throughput by up to 58.64% (resp. 96.54%) at high task loads.

Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in tasks requiring multimodal understanding. However, recent studies indicate that LVLMs are more vulnerable than LLMs to unsafe inputs and prone to generating harmful content. Existing defense strategies primarily include fine-tuning, input sanitization, and output intervention. Although these approaches provide a certain level of protection, they tend to be resource-intensive and struggle to effectively counter sophisticated attack techniques. To tackle such issues, we propose One-head Defense (Oh Defense), a novel yet simple approach utilizing LVLMs' internal safety capabilities. Through systematic analysis of the attention mechanisms, we discover that LVLMs' safety capabilities are concentrated within specific attention heads that respond differently to safe or unsafe inputs. Further exploration reveals that a single critical attention head can effectively serve as a safety guard, providing a strong discriminative signal that amplifies the model's inherent safety capabilities. Hence, the Oh Defense requires no additional training or external modules, making it computationally efficient while effectively reactivating suppressed safety mechanisms. Extensive experiments across diverse LVLM architectures and unsafe datasets validate our approach, i.e., the Oh Defense achieves near-perfect defense success rates (> 98\%) for unsafe inputs while maintaining low false positive rates (< 5\%) for safe content. The source code is available at https://github.com/AIASLab/Oh-Defense.

Differential privacy (DP) is the de facto privacy standard against privacy leakage attacks, including many recently discovered ones against large language models (LLMs). However, we discovered that LLMs could reconstruct the altered/removed privacy from given DP-sanitized prompts. We propose two attacks (black-box and white-box) based on the accessibility to LLMs and show that LLMs could connect the pair of DPsanitized text and the corresponding private training data of LLMs by giving sample text pairs as instructions (in the blackbox attacks) or fine-tuning data (in the white-box attacks). To illustrate our findings, we conduct comprehensive experiments on modern LLMs (e.g., LLaMA-2, LLaMA-3, ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, Claude-3, Claude-3.5, OPT, GPT-Neo, GPT-J, Gemma-2, and Pythia) using commonly used datasets (such as WikiMIA, Pile-CC, and Pile-Wiki) against both wordlevel and sentence-level DP. The experimental results show promising recovery rates, e.g., the black-box attacks against the word-level DP over WikiMIA dataset gave 72.18% on LLaMA2 (70B), 82.39% on LLaMA-3 (70B), 75.35% on Gemma-2, 91.2% on ChatGPT-4o, and 94.01% on Claude-3.5 (Sonnet). More urgently, this study indicates that these well-known LLMs have emerged as a new security risk for existing DP text sanitization approaches in the current environment.

Multi-Channel Fusion Attacks (MCFAs) boosts the effectiveness of side-channel cryptanalysis by combining information leakages from multiple channels such as power and electromagnetic (EM) leakages. However, current MFCAs require the same number of traces across the channels, which poses a critical constraint that does not necessarily hold in practical scenarios. For instance, acquiring contactless EM traces is considerably easier than obtaining power traces. Consequently, an attacker can collect many more EM traces, but a considerable portion of them must be discarded due to this constraint. To address the constraint of requiring equal numbers of traces across channels while fully utilizing the otherwise discarded EM traces, we propose a novel multi-channel fusion framework. This framework leverages a machine learning model to synthesize the missing power traces, thereby aligning the number of traces in the power and EM channels. As a result, it maximizes the utilization efficiency of the available traces. Specifically, we introduce GTL, a hybrid generative adversarial network that integrates Transformer and LSTM architectures for effective trace synthesis. We present the first MCFAs against the postquantum cryptographic scheme CRYSTALS-Dilithium. Experimental results show that, compared with existing MCFAs, our approach reduces the required number of traces by more than 50%.

Attribute-based encryption (ABE) provides fine-grained access control over encrypted data without restricting itself to a single access policy, making it applicable to diverse scenarios such as cloud environments. However, existing bilinear pairing-based ABE schemes are vulnerable to quantum attacks, while lattice-based ABE schemes typically lack flexible and efficient user or attribute revocation mechanisms. To address these challenges, this paper presents a fine-grained revocable attribute-based encryption (FR-ABE) scheme based on the ring learning with errors (RLWE) assumption. We introduce a two-dimensional attribute structure and an extended Shamir’s secret sharing method, which together support multi-valued attributes and flexible threshold access policies, while reducing storage and computational overhead. Furthermore, We have devised a fine-grained revocation mechanism that functions at both the attribute and user levels, thereby accommodating the frequent role and permission changes. The attribute authority centrally manages attribute revocation through an indirect revocation method, eliminating the need to re-run the sampling algorithm and instead relying solely on polynomial-level operations, which reduces time overhead. User revocation is implemented using a binary tree structure, which updates only the ciphertext and leaves all keys unchanged. Theoretical analysis suggests that our scheme performs well in terms of both computational and storage overhead, and experimental findings further corroborate its superiority. The security analysis establishes the scheme’s selective security under the RLWE assumption.

ABE as a powerful tool for secure fine-grained access control, has been widely adopted in data-sharing scenarios such as cloud computing. However, most existing traceable and revocable ABE schemes are constructed using bilinear pairings and are thus vulnerable to quantum attacks. Although lattice-based ABE schemes provide post-quantum security, they often lack critical features such as traitor tracing and timely revocation, which may lead to key abuse problem. Some revocable lattice-based schemes are unable to identify the source of leaked keys, while existing traceable and revocable lattice-based schemes also fail to resist collusion attacks between revoked and un-revoked users. In addition, the exposure of attribute values in access policies poses serious privacy risks. To solve the above issues, this paper proposes a traceable and revocable KP-ABE scheme based on RLWE assumption. The proposed scheme embeds the user identity into the key through a white-box tracing mechanism, enabling the system to detect and revoke malicious users in case of key leakage. It also protects attribute privacy by hiding attribute values in the access policy and resists collusion attacks by embedding random parameters in the key. Theoretical analysis and experimental evaluation results show that the proposed scheme ensures security and privacy while maintaining high computational efficiency and is suitable for post-quantum secure data sharing environments.

Searchable symmetric encryption (SSE) enables keyword search over encrypted data but inevitably leaks patterns that adversaries can exploit to infer sensitive information. Differential privacy (DP) has been adopted to mitigate such leakages efficiently, yet existing DP-enhanced schemes are limited to single-user settings, lacking support for multi-user data sharing under diverse privacy needs. In such settings, distinct encryption keys and heterogeneous privacy levels raise two critical challenges: (i) leakage amplification, a prevalent yet under-explored threat where adversaries infer keywords from low-privacy data and link them to high-privacy data in cross-user retrieval; and (ii) inefficient privilege management, where cross-user search requires cumbersome key transmissions and frequent updates due to changing privileges. To bridge these challenges, we propose DPE-MUSE, a DP-Enhanced Multi-User Searchable Encryption scheme that achieves secure mutual retrieval while hiding access and size patterns. At its core is an adaptive keyword-level budget allocation strategy that derives budgets from data distributions and mitigates cross-user inference. A unified key management strategy further eliminates direct key sharing and streamlines privilege updates. Experiments on the Enron dataset show that DPE-MUSE achieves high recall (98.96%) under strict privacy requirements with relative low overhead, requiring only 58.951 ms for budget computation on a dataset of size 214.

As one of the first post-quantum digital signature schemes standardized by NIST, Dilithium has gained significant attention due to its potential role in securing communication in the quantum era. However, ensuring the security of its practical implementations, particularly against fault attacks, remains a significant challenge. Despite various proposed countermeasures, their effectiveness remains inadequately explored. This paper presents a comprehensive analysis of fault attacks on Dilithium, introducing five distinct attack techniques across three fault types: instruction-skipping, loop-aborting, and zeroing, which target the polynomial structure and Number Theoretic Transform (NTT) operations in Dilithium. These attacks are shown to significantly reduce the number of faulty signatures required for private key recovery, with the most efficient technique requiring as few as one faulty signature. Our methods successfully compromise both the reference and the protected implementation designed to resist skip-addition attacks. Experimental validation on an ARM Cortex-M4 platform demonstrates the effectiveness of these attacks, with success rates ranging from 29% to 63%, enabling full private key recovery in both deterministic and randomized Dilithium implementations. These findings underscore the need for more robust, fault-resilient post-quantum cryptographic implementations.

Abstract Sharing electronic protected health information (ePHI) is highly beneficial in public affairs. Constructing a cloud-assisted ePHI retrieval service represents a modern strategy that enhances cost-effectiveness and efficiency, making it a promising solution. However, widespread implementation is hindered by concerns over privacy violations. Symmetric Searchable Encryption (SSE) has emerged as a practical approach for ensuring data privacy and efficient retrieval in the cloud without decryption. Nonetheless, existing SSE schemes encounter challenges, including secure and efficient data updates, effective search-update permission control, and cross-platform adaptability. To address these limitations, we propose a novel variant of Dynamic Searchable Symmetric Encryption known as SEAC, tailored for secure, cloud-assisted sharing of ePHI. SEAC is formalized within a multi-client/single-server model, enabling authorized clients to perform search or update on encrypted data. The concrete construction integrates dynamic cryptographic accumulators with an efficient binding mechanism for index structures, offering two key features: (1) sublinear conjunctive keyword search that ensures forward and Type-II backward privacy, and (2) lightweight, updatable permission control that effectively mitigates read-write confusion while supporting scalable user revocation and re-authorization. We developed both a localized and a web-based implementation, demonstrating its efficiency and scalability in practical applications.

Abstract Lattice-based attribute-based encryption (ABE) combines the advantages of against quantum attack and fine-grained access control. However, most existing LWE-based or RLWE-based ABE schemes from lattices have the limitations of large storage cost and lack of support for attribute privacy protection. In 2022, a new algebraically structured LWE variant-cyclic algebra LWE (CLWE), was proposed by Grover et al. in journal of cryptology (JoC). This new variant has its inherent storage and computing advantages, especially in reducing the storage overhead. Therefore, to reduce the storage cost and protect the attribute privacy, we propose a novel lattice-based ABE scheme based on CLWE. More specifically, by introducing an extended Shamir’s secret sharing scheme on cyclic algebra and a two-dimensional (attribute label, attribute value) attribute structure, we extend the CLWE-based PKE scheme in JoC22 to a CLWE-based ABE scheme. The sizes of public key, master secret key, user’s secret key and ciphertext of our proposal are remarkably reduced. In addition, we combines a semi access policy structure and the two-dimensional attribute structure to hide the user’s attribute values, thereby preventing the leakage of user attribute privacy. Performance analysis shows that compared related lattice-based ABE schemes, our proposal is more efficient in the storage cost and it supports attribute privacy protection. Finally, our scheme is proved to be secure in the standard model.

Graph databases, which essentially store network nodes and edge relationships between them, offer a promising solution for managing the large and dynamic Internet of Things (IoT) network. However, as data grows explosively, end devices cannot carry it, forcing organizations to outsource storage to cloud servers, bringing privacy risks, such as data leakage. Existing privacy-preserving graph search schemes either fail to support secure and efficient multigranularity updates over encrypted complicated property graph or neglect multiuser access control, greatly limiting their practicability. In this article, we propose a novel dynamic encrypted property graph search system along with three full-fledged constructions, named Spidey. We model the property graph and introduce two well-designed structures: bidirectional index and delete list, which form the foundation of our schemes. The basic schemeDGraphsupports efficient, fine-grained sublinear queries and updates with the complexity of both attribute-grained update and node-grained deletion being$\mathcal {O}(1)$, while ensuring both forward privacy (FP) and backward privacy (BP). Two enhanced schemes$\mathtt {DGraph\_RW}$and$\mathtt {DGraph\_Role}$further incorporate lightweight operation-based and (hierarchical) role-based access control, respectively, while avoiding encrypted index expansion and minimizing the impact on search efficiency. Both theoretical comparison and experiment results demonstrate their usability and scalability. Notably, for attribute-grained update,DGraphis$2.5\times $faster than ODXT (by Patranabis and Mukhopadhyay), and for node-grained deletion, with each node associated with 12 attributes,DGraphis$30\times $faster than ODXT.

Deep models excel in analyzing image data. However, recent studies on Black-Box Model Inversion (MI) Attacks against image models have revealed the potential to recover concealed (via specific masks) private training images using publicly available images from the same domain as the training data. This study introduces PriDM, a novel diffusion model-based MI attack, illustrating the increased vulnerability of image models. PriDM leverages range-null space decomposition to extract essential range-space information and incorporates it into the diffusion model's sampling process. This enables the recovery of private information from arbitrarily masked images relying solely on images only aligned with the same machine-learning tasks as the target model. To demonstrate PriDM's effectiveness, we conducted experiments with various adversary background knowledge, including different public dataset domains and image masks. Results show PriDM produces recovered images of significantly higher quality, approximately twice as good as existing methods. Moreover, in scenarios involving complex backgrounds, PriDM outperforms the state-of-the-art by approximately 70%. In specific background knowledge scenarios, such as compressed and blurred images, our method achieves an almost 100% success rate. Additionally, PriDM performs well with real-world background knowledge including individuals wearing masks and randomly masked face images, which are not considered by existing works.

Misuse resilience is an important research topic in the NIST PQC standardization process. Key mismatch attacks are a major type of misuse attack. In key mismatch attacks, the adversary aims to recover the reused secret key by sending special ciphertexts to the target party and observing whether the shared key matches his guesses or not.

Post-Quantum Cryptography (PQC) was proposed due to the potential threats quantum computer attacks against conventional public key cryptosystems, and four PQC algorithms besides CRYSTALS-Dilithium (Dilithium for short) have so far been selected for National Institute of Standards and Technology (NIST) standardization. However, the selected algorithms are still vulnerable to side-channel attacks in practice, and their physical security need to be further evaluated. This paper proposes two efficient power analysis attacks against Dilithium implementation, the optimized fast two-stage approach and the single-bit approach, aiming at reducing the key guess space. Our findings reveal that the optimized approach outperforms the conservative approach and the fast two-stage approach proposed in ICCD 2021 by factors of 338 and 49, respectively. Similarly, compared to these two approaches, the single-bit approach achieves acceleration of 367 times and 53 times, respectively.

An accurate and efficient trust evaluation mechanism is the cornerstone of maintaining the stability of SDN networks, especially in face of the escalating threats like Distributed Denial of Service (DDoS) attacks. The traditional trust evaluation mechanisms in SDN often lack of adaptability and accuracy, due to that they typically rely on the direct trust or ignore the influence of indirect and historical trust factors which lead to inaccurate trust evaluation and lower efficiency. In order to solve these problems, we propose a novel multidimensional trust evaluation mechanism consisting of three sub-modules: Device Behavior Trust Evaluation Scheme (DBTES), Machine Learning-based Trust Evaluation Scheme (MLTES), and Bayesian-Based Trust Evaluation Scheme (BBTES). These modules work together to enhance the precision and adaptability of trust assessments by capturing various aspects of device behavior. Additionally, we introduce a trust fusion algorithm that combines the Analytical Hierarchy Process(AHP) with Criteria Importance Through Inter-criteria Correlation (CRITIC) to optimize weight distribution, further improving the accuracy and robustness of trust evaluations. Our approach overcomes the limitations of existing methods by providing a more comprehensive and adaptable trust assessment. Simulation results show that under varying Malicious Device Ratio conditions, the MDTM method improves the task success rate by up to 4.39% compared to traditional methods, along with an increase in average trust values by 5.16%. Additionally, with changing historical factors, MDTM further improves trust values by 5.33%. These results demonstrate the enhanced resilience and effectiveness of our approach in maintaining network stability and security within SDN environments.

Software-Defined Networking (SDN) has introduced flexibility and efficiency to network management but also faces challenges from flow rule conflicts, including static, dynamic, and dependency conflicts. Existing detection algorithms often focus on a single conflict type, resulting in inefficiencies and high false positive rates. To address these issues, we propose a comprehensive flow rule conflict detection scheme that improves real-time detection of explicit conflicts (both static and dynamic) and reduces false positives in implicit (dependency) conflict detection. Specifically, we present a real-time explicit conflict detection algorithm based on the Protocol-Divided Trie (PDT), which categorizes flow rules by protocol type and uses a prefix tree for rapid matching. Experimental results show that this approach significantly reduces detection times by at least 39.2% and achieves 100% detection accuracy. Additionally, we propose a two-stage detection (TSD) algorithm that combines the precision of path-based detection (PBD) with the efficiency of alias set-based detection (ASD). Our experiments reveal a 51% reduction in false positives compared to ASD and a 48% reduction in detection time compared to PBD, while maintaining equivalent false positive rates. This approach provides a robust solution for conflict detection in SDN, improving network security and resource utilization efficiency.

This paper investigates high-performance implementations of Falcon scheme, which is one of the four post-quantum cryptography algorithms standardized by NIST. We explore high-throughput implementation solutions by improving how critical components of Falcon execute on the GPU, with a focus on low latency requirements. Our research reveals that some components of Falcon cannot fully exploit the parallelism of the GPU. Consequently, we propose a heterogeneous parallel implementation of the Falcon signature scheme that utilizes the parallelism of both CPUs and GPUs, while mitigating the additional overhead introduced by heterogeneous computing. Finally, we conduct evaluations on three typical testbeds. The experimental results show that our improved GPU implementation is 222 percent faster for signing and 13.9 percent faster for verification in cloud scenarios compared to the state-of-the-art GPU implementations. On an embedded GPU platform (Jetson AGX Orin), our heterogeneous parallel implementation outperforms the CPU multi-threaded implementation by 46 percent and the GPU implementation by 297 percent.

Side-channel leakage acquisition plays a crucial role in side-channel analysis against cryptographic implementations, since it usually has a decisional impact on the security claims of the target devices. While most existing research has concentrated on power consumption acquisition settings, the exploration of electromagnetic (EM) radiation leakage acquisition remains limited and surprisingly under-discussed. In this study, we systematically investigate the parameter setting for EM leakage acquisition across two devices of different architectures. We specifically examine the effects of the amplifier, coupling mode, sampling rate, and EM probe on the quality of collected EM traces. For the first device STM32F405, our proposed optimal acquisition settings enhance the signal-to-noise ratio by a factor of 16 compared to the reference settings and reduce the number of EM traces required to achieve a success rate of 90% by a factor of 38. For the second device ATmega2560, the optimal settings improve the signal-to-noise ratio by a factor of 400 compared to the reference settings and reduce the number of EM traces required to achieve a success rate of 90% by a factor of 320. In summary, this work offers a comprehensive investigation into high-quality EM leakage acquisition. While some conclusions may be specific to certain devices, we believe that the proposed guidelines can be applied to EM trace acquisition in other devices as well.

Ascon algorithm has been selected by NIST for standardization of the lightweight cryptography, that will be used in embedded systems, IoT devices, and other resource-constrained devices. As a lightweight cryptographic algorithm, Ascon implementation requires less computation and memory. However, the current implementations of Ascon algorithms and existing protection schemes are vulnerable to side-channel attacks. In this paper, we propose an IPM (inner product masking) based protection for Ascon implementations. Our new masking scheme can prevent side-channel attacks from successfully retrieving the key, even when the leakage levels are nearly identical. Alternatively, the cost of obtaining the key can increase by several orders of magnitude, making the attack significantly more challenging. The proposed scheme is experimentally validated by porting it into a STM32F407 microcontroller and conducting correlation power analysis (CPA) and template attack (TA) on the collected power traces. The experimental results show that while CPA can successfully retrieve the keys from both the unprotected Ascon implementation and the Boolean-masked Ascon implementation, they fail against the new masking scheme. Although TA attacks can break the new scheme, they require approximately 50 times more data compared to attacking the Boolean mask. This demonstrates that the new masking scheme offers significantly greater security than both the Boolean mask and the unprotected implementations.

Fully homomorphic encryption (FHE) scheme allows for performing computations on encrypted data without decrypting it which makes it more popular in various domains. Most previous FHE constructions are limited by the large ciphertext expansion rate and error growth rate. Learning with errors (LWE) problem is a popular primitive for constructing lattice-based FHE. Many algebraic variants of LWE are also developed with further promising cryptographic features. Most previous known LWE variants (over the commutative base rings) are unified into a general framework by Peikert et al. at TCC 2019. In 2022, a new algebraically structured LWE problem over the d-degree cyclic algebra (CLWE) (with modulus q), a typical non-commutative ring, was proposed by Grover et al.. To further explore the utility and potential advantages of this new variant, it is interesting to design new lattice-based FHE schemes by using CLWE. In this paper, by following the diagrams of Gentry-Sahai-Waters (GSW13 for short), we propose a FHE scheme based on the CLWE with an even small ciphertext size. Due to our further expansion of the plaintext space, the average ciphertext expansion rate (CER for short) is smaller. More precisely, compared to original GSW13 and the related GSW-style constructions, the ciphertext size of our proposal is reduced by almost at least 23.69%, and the average CER is reduced about 96% due to our expansion of the plaintext space. Besides, our FHE has an even small error growth rate (EGR for short). After one time multiplication of two ciphertexts, the asymptotical EGR is reduced about 49.8% compared to the original GSW13 and the related GSW-style constructions. Typically, the smaller ciphertext size, CER and EGR make our FHE scheme more efficient in trusted computing environment.

For the high-performance single-board computers (SBCs) running an operating system, side-channel attacks usually come at high analytical costs, requiring millions of traces. This work uses the case of electromagnetic attacks on encryption services in real-world SBCs to explore how various preprocessing methods can reduce the attack costs for such devices. Specifically, we propose a general preprocessing framework that effectively combines multiple preprocessing methods based on their inherent characteristics. Utilizing this framework, we design a four-layer preprocessing scheme that significantly reduces the number of traces for key recovery. The experimental results show that, when the attack success rate reaches 80 percent, the proposed preprocessing scheme reduces the number of traces required by a factor of approximately 10 compared to the latest alignment algorithm on the Raspberry Pi 2B. Our research demonstrates the feasibility of conducting low-cost side-channel attacks on SBCs, further emphasizing the need to protect sensitive applications running on these devices.

Masking is widely considered as an effective countermeasure against side-channel analysis (SCA) due to its provable security and efficiency. However, recent works have demonstrated that the deep learning-based SCA (DL-SCA) can effectively break the cryptographic implementations protected by the first-order Boolean maskings. Still, it is open whether higher-order masking can resist DL-SCA. In this work, we demonstrate that deep learning methods can also effectively exploit the inherent leakage of higher-order Boolean masking to compromise its security. Furthermore, we employed neural weight visualization techniques to demonstrate the neural network’s capability to extract high-level features. We assess the efficiency of this novel profiling attack in both simulated and real-world scenarios. In particular, our results show that DL-SCA can effectively break the higher-order Boolean masking schemes up to the sixth and the third order in simulated and real-world cases, respectively. Furthermore, we find that using plaintext-related leakage can significantly improve the effectiveness of side-channel attacks.

Higher-order masking is an effective countermeasure against side-channel attacks but is often perceived as impractical due to its cost. Recent advancements in parallelization technologies have made higher-order masking more feasible. However, the security of these implementations can be jeopardized by the parallelization methods used, as their theoretical assurances may not hold in real-world scenarios. In this paper, we improve the security and efficiency of higher-order masking schemes for block ciphers through a refined bit-sliced implementation. Our method addresses lower-order leakage caused by bit interactions, which are prevalent in current schemes. We evaluated the efficiency of our approach across various widely used ARM and AVR platforms, demonstrating efficiency improvements across all test platforms. By optimizing the masking gadgets, our approach reduces the required clock cycles by 30% to 50% compared to the original implementation, across share numbers from 2 to 32 on the 32-bit ARM platform. We validate the enhanced security of our method through both theoretical analysis and practical leakage detection, proving its effectiveness against bit interaction leakage.

In recent years, Out-of-Distribution (o.o.d) Robustness has garnered increasing attention in Deep Learning, and shape-biased Convolutional Neural Networks (CNNs) are believed to exhibit higher robustness, attributed to the inherent shape-based decision rule of human cognition. In this work, we delve deeper into the intricate relationship between shape/texture information and o.o.d robustness by leveraging a carefully curated "Category-Balanced ImageNet" dataset. We find that shape information is not always superior in distinguishing distinct categories and shape-biased model is not always superior across various o.o.d scenarios. Motivated by these insightful findings, we design a novel method named Shape-Texture Adaptive Recombination (STAR) to achieve higher o.o.d robustness. A category-balanced dataset is firstly used to pretrain a debiased backbone and three specialized heads, each adept at robustly extracting shape, texture, and debiased features. Subsequently, an instance-adaptive recombination head is trained to adaptively adjust the contributions of these distinctive features for each given instance. Through comprehensive experiments, our proposed method achieves state-of-the-art o.o.d robustness across various scenarios such as image corruptions, adversarial attacks, style shifts, and dataset shifts, demonstrating its effectiveness.

Abstract Partial key exposure attacks present a significant threat to RSA-type cryptosystems. These attacks factorize the RSA modulus by utilizing partial knowledge of the decryption exponent, which is typically revealed by side-channel attacks, cold boot attacks, etc. In practice, the RSA implementations typically employ countermeasures to resist physical attacks, such as additive exponent blinding $$d' = d + r \varphi (N)$$ d ′ = d + r φ ( N ) with unknown random blinding factor r. Although there are a couple of partial key exposure attacks on blinding RSA, these attacks require a considerable amount of leakage and fail to work when e is up to full size. In this paper, we propose new partial key exposure attacks on RSA with additive exponent blinding, focusing on leakage scenarios where the Most Significant Bits (MSBs) or Least Significant Bits (LSBs) of $$d'$$ d ′ are revealed. For the case where e is small, we first recover partial information of p by solving the quadratic congruence equation, and then find the small roots of the integer equation to recover entire private key. Our method relaxes the attack requirements, for instance, we reduce the amount of MSBs for a successful attack from 75 to 25% when $$e \approx N^{0.25}$$ e ≈ N 0.25 and $$r\approx N^{0}$$ r ≈ N 0 . Furthermore, we propose new attacks using the unique algebraic relationship in blinding RSA, which extend the attack to the case where e is of full size.

Abstract During the standardisation process of post-quantum cryptography, NIST encourages research on side-channel analysis for candidate schemes. As the recommended lattice signature scheme, CRYSTALS-Dilithium, when implemented on hardware, has seen limited research on side-channel analysis, and current attacks are incomplete or requires a substantial quantity of traces. Therefore, we conducted a more complete analysis to investigate the leakage of an FPGA implementation of CRYSTALS-Dilithium using the Correlation Power Analysis (CPA) method, where with a minimum of 70,000 traces partial private key coefficients can be recovered. Furthermore, we optimise the attack by extracting Point-of-Interests using known information due to parallelism (named CPA-PoI) and by iteratively utilising parallel leakages (named CPA-ITR). Our experimental results show that CPA-PoI reduces the number of traces by up to 16.67%, CPA-ITR by up to 25%, and both increase the number of recovered key coefficients by up to 55.17% and 93.10% using the same number of traces. They outperfom the CPA method. As a result, it suggests that the FPGA implementation of CRYSTALS-Dilithium is more vulnerable than thought before to side-channel analysis.

Success Rate (SR) is one of the most popular side-channel security metrics measuring the efficiency of key recovery. Theoretical expression of success rate reveals the functional dependency between relevant parameters such as number of measurements and Signal-to-Noise Ratio (SNR), helping researchers understand the resistance of a given implementation rapidly. However so far, existing works have exposed fundamental problems: (1) Evaluation is confined to a very limited range of distinguishers and specialized methods; (2) Evaluation assumes a perfect leakage model that is detached from reality. It is widely observed that an inaccurate leakage model will lead to a degraded or even distorted success rate. In this paper, we tackle above problems by introducing a novel framework which is able to evaluate seven side-channel distinguishers with a unified expression. Among them, we explore four new distinguishers that have not been investigated in the existing literature. Within the framework, DPA distinguishers are intuitively understood as linear maximum likelihood attack testing closeness between vectors with some easy-to-comprehend geometric metrics. Our evaluation is able to deal with profiled models of any quality and is agnostic to model profiling techniques. It uniquely enables the evaluation of success rates under inaccurate leakage models, whilst providing an (indirect) answer to the open question “how much information is lost due to the model biases” through quantifying the degradation of success rates. Finally, we formulate a set of criterion values for quantitative analyses of the model biases. It provides theoretical evidences for a more thorough explanation for the various behaviors of DPA attacks. Experimental results are inline with the theory, confirming its practical applicability.

SIMD extensions are widely adopted in multi-core processors to exploit data-level parallelism. However, when co-running workloads on different cores, compute-intensive workloads cannot take advantage of the underutilized SIMD lanes allocated to memoryintensive workloads, reducing the overall performance. This paper proposes Occamy, a SIMD co-processor that can be shared by multiple CPU cores, so that their co-running workloads can spatially share its SIMD lanes. The key idea is to enable elastic spatial sharing by dynamically partitioning all the SIMD lanes across different workloads based on their phase behaviors, so that each workload may execute in variable-length SIMD mode. We also introduce an Occamy compiler to support such variable-length vectorization by analyzing such phase behaviors and generating the vectorized code that works with varying vector lengths. We demonstrate that Occamy can improve SIMD utilization, and consequently, performance over three representative SIMD architectures, with negligible chip area cost.

is a popular countermeasure due to its provable security. Table re-computation based Boolean masking (BM) is efficient at small masking share number, and addition chain based inner product masking (IPM) provides higher security order than BM. As a result, the natural question is: can we design a masking scheme that costs close to that of re-computation based BM while providing security comparable to that of addition chain based IPM? In this paper, we propose a table re-computation based IPM scheme that provides 3rd-order security while being slightly more expensive than table re-computation based BM. Furthermore, we improve the side-channel security of IPM by randomly selecting the parameter$L$from an elaborated low entropy set, which we call low entropy inner product masking (LE-IPM). In an Intel Core i7-4790 CPU and ARM Cortex M4 based MCU for AES, we implemented four masking schemes, namely the addition chain based IPM and table re-computation based BM, IPM, and LE-IPM. Our proposals perform slightly slower (by about 0.8 times) than table re-computation based BM but significantly faster (at least 30 times) than addition chain based IPM. Furthermore, we assess the security of our proposals using a standard method named test vector leakage assessment methodology (TVLA). Our proposals provide the expected security against side-channel attacks according to the evaluation.

Abstract Preparing a large number of physical traces is an important first step in Side-Channel Analysis, especially in Deep-Learning based Side-Channel Analysis (DL-SCA). With sufficient training data and a proper modeling algorithm, the secret key of cryptographic devices can be successfully recovered with a small number of attacking data. However, in reality, it may be impossible or difficult, in some threat models, to collect sufficient data due to various resource constraints. In this case, the performance of DL-SCA will be severely decreased. In this work, we propose an easy-to-implement method to achieve an efficient DL-SCA with a small number of training data in the scenario of software-based cryptographic implementations. Our simultaneously multi-byte training method, which trains the model with side-channel leakage characteristics of different byte intermediate values, significantly enhances the robustness and performance of DL-SCA. The simulated experiment shows that our method achieves more robust profiling. The success rate of recovering a secret AES key can be improved by 250% with the same collected data. The results of attacking real-world COTS USIM cards are consistent with the ones of simulation-based counterparts. Compared with state-of-the-art data-augmentation techniques, our results show that the proposed method can achieve the same or even better performance without additional generated training data.

Abstract As a promising service paradigm, cloud computing has attracted lots of enterprises and individuals to outsource big data to public cloud. To facilitate secure data using and sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. However, some serious challenges are still remaining toward achieving flexible and controllable access policy update in CP-ABE, which essentially impede the powerful access control ability of CP-ABE from long-term and large-scale deployment in real systems. In this work, we propose a novel scheme named policy updatable CP-ABE for encrypted data sharing scenes. The proposed scheme features the following achievements: (i) it supports fine-grained update algorithms with no restriction on update time; (ii) the cloud server can effectively verify update ciphertexts, so the integrity of original data would not be compromised intentionally or accidentally during the update and (iii) most operations of encryption and policy update are securely outsourced to cloud servers, leaving extremely low overheads for data owners and users. We formally define its security model and prove it is adaptively secure. Also, we implement the proposed scheme using the Charm framework. The experiment results demonstrate that it is efficient and practical.

Abstract Side-channel analysis (SCA) has become an increasing important method to assess the physical security of cryptographic systems. In the process of SCA, the number of attack data directly determines the performance of SCA. With sufficient attack data, the adversary can achieve a successful SCA. However, in reality, the cryptographic device may be protected with some countermeasures to limit the number of encryptions using the same key. In this case, the adversary cannot use casual numbers of data to perform SCA. The performance of SCA will be severely dropped if the attack traces are insufficient. In this paper, we introduce wavelet scatter transform (WST) and short-time fourier transform (STFT) to non-profiled side-channel analysis domains, to improve the performance of side-channel attacks in the context of insufficient data. We design a practical framework to provide suitable parameters for WST/STFT-based SCA. Using the proposed method, the WST/STFT-based SCA method can significantly enhance the performance and robustness of non-profiled SCA. The practical attacks against four public datasets show that the proposed method is able to achieve more robust performance. Compared with the original correlation power analysis (CPA), the number of attack data can be reduced by 50–95%.

Side-channel security has become a significant concern in the NIST post-quantum cryptography standardization process. The lattice-based CRYSTALS-Dilithium (abbr. Dilithium) becomes the primary signature standard algorithm recommended by NIST for most use cases in July 2022 due to its excellent performance in security and efficiency. Compared to Dilithium’s rich theoretical security analysis results, the side-channel security of its physical implementations needs to be further explored. In 2021, Liu et al. proposed a two-stage randomness leakage attack against Dilithium, in which only one randomness bit with a probability$> 0.5$per signature is enough to recover the private key. However, they only carried out proof-of-concept experiments on “research-oriented” reference implementation of polynomial addition. Whether this method applies to complete real-world implementations of Dilithium is unknown. In this paper, we put this randomness leakage attack into real-world and recover the private key of unprotected and masked Dilithium on Arm Cortex-M4 processor using non-profiled power analysis attacks. Since randomness is introduced in the signing process, it is challenging to recover the randomness bit of Dilithium with high success rate in only one trace. Inspired by Liu et al., we propose a new non-profiled attack called Public Template Attack (PTA), a template-attack-like method that builds templates using public information. With PTA, we recover the randomness bit of unprotected and masked Dilithium with a success rate of 95% and 62% in one power trace, respectively. To demonstrate practicality, we perform practical power analysis attacks against different security levels of round 3 unprotected and masked Dilithium on STM32F405 microprocessor. Using 10,000 traces, the private key of unprotected Dilithium2 is recovered in 0.5 hours with an ordinary PC desktop. Our attack is 240 times faster than the state-of-the-art non-profiled attack. Moreover, the private key of masked Dilithium2 is recovered using 680,000 traces in 38 hours. To the best of our knowledge, we are the first to successfully attack masked Dilithium using non-profiled attacks.

Abstract Gaussian sampling over the integers is one of the fundamental building blocks of lattice-based cryptography. Among the extensively used trapdoor sampling algorithms, it is ineluctable until now. Under the influence of numerous side-channel attacks, it is still challenging to construct a Gaussian sampler that is generic, efficient, and resistant to timing attacks. In this paper, our contribution is three-fold. First, we propose a secure, efficient exponential Bernoulli sampling algorithm. It can be applied to Gaussian samplers based on rejection samplings. We apply it to FALCON, a candidate of round 3 of the NIST post-quantum cryptography standardization project, and reduce its signature generation time by 13–14%. Second, we develop an isochronous Gaussian sampler based on rejection sampling. Our Algorithm can securely sample from Gaussian distributions with different standard deviations and arbitrary centers. We apply it to PALISADE (S&P 2018), an open-source lattice-based cryptography library. During the online phase of trapdoor sampling, the running time of the G-lattice sampling algorithm is reduced by 44.12% while resisting timing attacks. Third, we improve the efficiency of the COSAC sampler (PQC 2020). The new COSAC sampler is 1.46x–1.63x faster than the original and has the lowest expected number of trials among all Gaussian samplers based on rejection samplings. But it needs a more efficient algorithm sampling from the normal distribution to improve its performance.

In recent years, inner product masking (IPM) has been proposed as a promising code based masking scheme against side-channel attacks. However, most studies mainly focus on leakages in terms of information-theoretic metrics, and simulated experiments utilizing profiled attacks (with known templates). In this paper, we investigate the security of IPM scheme against one typical non-profiled side-channel attack, namely higher-order correlation analysis. We demonstrate that three factors mainly influence the efficiency of higher-order correlation analysis against IPM, and they are the attack order vector, output size of the target function and attack model. In particular, we propose a new method to measure the efficiency of higher-order correlation analyses. Generally speaking, this method is based on the correlation coefficient between leakage expectations with certain shares and sensitive values under the attack model. We validate our method by both simulation-based experiments and practical one with different attack factors. All experiments are running on the IPM in the different noise scenarios. The results demonstrate that our method works well as an indicator for higher-order correlation analyses against IPM.

Human-chosen text passwords remain an important form of authentication. To better understand properties of passwords chosen by users and password guessing models adopted by attackers, a lot of researches on password guessing are carried out. In recent studies, people try to guess passwords through deep learning models. However, existing deep learning-based guessing models (such as RNN and GAN) show unsatisfactory performance under limited guesses. In this paper, we propose PGVAE, a password guessing model based on variational autoencoder. The model can learn highly structured and continuous latent representations of passwords and then generate highquality candidate guesses. The effectiveness of PGVAE is verified in multiple leaked dataset. Results show that our model is capable of modeling the guessability of passwords, detecting vulnerable passwords and strengthening the security of authentication systems.

Due to its provable security and remarkable device-independence, masking has been widely accepted as a good algorithmic-level countermeasure against side-channel attacks. Subsequently, several code-based masking schemes are proposed to strengthen the original Boolean masking (BM) scheme, and Inner Product Masking (IPM) scheme is typically one of those. In this paper, we provide a framework, named analysis with predicted template (APT), for side-channel analysis against the IPM scheme. Following this framework, we propose two attacks based on maximum likelihood and Euclidean distance, respectively. To evaluate their efficiency, we perform simulated experiments on first-order BM and an optimal IPM scheme. The results show that our proposals are equivalent to a second-order CPA against BM scheme, but they are significantly efficient against an optimal IPM. In practical experiments based on an ARM Cortex-M4 architecture, the results of our proposals do not turn out well because of a few outliers in collected leakages. After filtering out these outliers, our proposals perform efficiently as expected. Finally, we argue that the side-channel security of IPM can be improved by keeping the vector L to be randomly selected from an elaborated small set.

Abstract Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations. In most cases, non-linear components (e.g. S-Boxes) of cryptographic algorithms will be chosen as primary targets of side-channel attacks (SCAs). In order to measure side-channel resistance of S-Boxes, three theoretical metrics are proposed and they are reVisited transparency order (VTO), confusion coefficients variance (CCV), and minimum confusion coefficient (MCC), respectively. However, the practical effectiveness of these metrics remains still unclear. Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples, this paper takes a comprehensive study of the applicability of these metrics. First of all, we empirically investigate the relations among three metrics for targeted S-boxes, and find that CCV is almost linearly correlated with VTO, while MCC is inconsistent with the other two. Furthermore, in order to verify which metric is more effective in which scenarios, we perform simulated and practical experiments on nine 4-bit S-Boxes under the non-profiled attacks and profiled attacks, respectively. The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks, VTO and CCV are more reliable while MCC fails. We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs. Finally, we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes. Unfortunately, all of them are invalid in this scenario.

Abstract Due to its provable security and remarkable device-independence, masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks. However, relatively high cost of masking severely limits its applicability. Considering the high tackling complexity of non-linear operations, most masked AES implementations focus on the security and cost reduction of masked S-boxes. In this paper, we focus on linear operations, which seems to be underestimated, on the contrary. Specifically, we discover some security flaws and redundant processes in popular first-order masked AES linear operations, and pinpoint the underlying root causes. Then we propose a provably secure and highly efficient masking scheme for AES linear operations. In order to show its practical implications, we replace the linear operations of state-of-the-art first-order AES masking schemes with our proposal, while keeping their original non-linear operations unchanged. We implement four newly combined masking schemes on an Intel Core i7-4790 CPU, and the results show they are roughly 20% faster than those original ones. Then we select one masked implementation named RSMv2 due to its popularity, and investigate its security and efficiency on an AVR ATMega163 processor and four different FPGA devices. The results show that no exploitable first-order side-channel leakages are detected. Moreover, compared with original masked AES implementations, our combined approach is nearly 25% faster on the AVR processor, and at least 70% more efficient on four FPGA devices.

Researchers proposed several data-driven methods to efficiently guess user-chosen passwords for password strength metering or password recovery in the past decades. However, these methods are usually evaluated under ad hoc scenarios with limited data sets. Thus, this motivates us to conduct a systematic and comparative investigation with a very large-scale data corpus for such state-of-the-art cracking methods. In this paper, we present the large-scale empirical study on password-cracking methods proposed by the academic community since 2005, leveraging about 220 million plaintext passwords leaked from 12 popular websites during the past decade. Specifically, we conduct our empirical evaluation in two cracking scenarios, i.e., cracking under extensive-knowledge and limited-knowledge. The evaluation concludes that no cracking method may outperform others from all aspects in these offline scenarios. The actual cracking performance is determined by multiple factors, including the underlying model principle along with dataset attributes such as length and structure characteristics. Then, we perform further evaluation by analyzing the set of cracked passwords in each targeting dataset. We get some interesting observations that make sense of many cracking behaviors and come up with some suggestions on how to choose a more effective password-cracking method under these two offline cracking scenarios.

Many enterprises and individuals prefer to outsource data to public cloud via various pricing approaches. One of the most widely-used approaches is the pay-as-you-go model, where the data owner hires public cloud to share data with data consumers, and only pays for the actually consumed services. To realize controllable and secure data sharing, ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution, which can provide fine-grained access control and encryption functionalities simultaneously. But there are some serious challenges when applying CP-ABE in pay-as-you-go. First, the decryption cost in ABE is too heavy for data consumers. Second, ABE ciphertexts probably suffer distributed denial of services (DDoS) attacks, but there is no solution that can eliminate the security risk. At last, the data owner should audit resource consumption to guarantee the transparency of charge, while the existing method is inefficient. In this work, we propose a general construction named fully accountable ABE (FA-ABE), which simultaneously solves all the challenges by supporting all-sided accountability in the pay-as-you-go model. We formally define the security model and prove the security in the standard model. Also, we implement an instantiate construction with the self-developed library$\mathsf{ libabe}$. The experiment results indicate the efficiency and practicality of our construction.

Side-channel attacks are one of the greatest practical threats to security-related applications, because they are capable of breaking ciphers that are assumed to be mathematically secure. Lots of studies have been devoted to power or electro-magnetic (EM) analysis against desktop CPUs, mobile CPUs (including ARM, MSP, AVR, etc) and FPGAs, but rarely targeted modern GPUs. Modern GPUs feature their special and specific single instruction multiple threads (SIMT) execution fashion, which makes their power/EM leakage more sophisticated in practical scenarios. In this article, we study side-channel attacks with leakage from SIMT systems, and propose leakage models suited to any SIMT systems and specifically to CUDA-enabled GPUs. Afterwards, we instantiate the models with a GPU AES implementation, which is also used for performance evaluations. In addition to the models, we provide optimizations on the attacks that are based on the models. To evaluate the models and optimizations, we run the GPU AES implementation on a CUDA-enabled GPU and, at the same time, collect its EM leakage. The experimental results show that the proposed models are more efficient and the optimizations are effective as well. Our study suggests that GPU-based cryptographic implementations may be much vulnerable to microarchitecture-based side-channel attacks. Therefore, GPU-specific countermeasures should be considered for GPU-based cryptographic implementations in practical applications.

Leakages during the signing process, including partial key exposure and partial (or complete) randomness exposure, may be devastating for the security of digital signatures. In this work, we investigate the security of lattice-based Fiat-Shamir signatures in the presence of randomness leakage. To this end, we present a generic key recovery attack that relies on minimum leakage of randomness, and then theoretically connect it to a variant of Integer-LWE (ILWE) problem. The ILWE problem, introduced by Bootle et al. at Asiacrypt 2018, is to recover the secret vector s given polynomially many samples of the form (a, 〈a〉, s)+e) ϵ ℤn+1, and it is solvable if the error e ϵ ℤ is not superpolynomially larger than the inner product (a, s). However, in our variant (we call the variant FS-ILWE problem in this paper), a ϵ ℤnis a sparse vector whose coefficients are NOT independent any more, and e is related to a and s as well. We prove that the FS-ILWE problem can be solved in polynomial time, and present an efficient algorithm to solve it. Our generic key recovery method directly implies that many lattice-based Fiat-Shamir signatures will be totally broken with one (deterministic or probabilistic) bit of randomness leakage per signature. Our attack has been validated by experiments on two NIST PQC signatures Dilithium and qTESLA. For example, as to Dilithium-III of 125-bit quantum security, the secret key will be recovered within 10 seconds over an ordinary PC desktop, with about one million signatures. Similarly, key recovery attacks on Dilithium under other parameters and qTESLA will be completed within 20 seconds and 31 minutes respectively. In addition, we also present a non-profiled attack to show how to obtain the required randomness bit in practice through power analysis attacks on a proof-of-concept implementation of polynomial addition. The experimental results confirm the practical feasibility of our method.

Abstract We revisit the definition of transparency order (TO) and that of modified transparency order (MTO) as well, which were proposed to measure the resistance of substitution boxes (S-boxes) against differential power analysis (DPA). We spot a definitional flaw in original TO, which is proved to significantly affect the soundness of TO. Regretfully, MTO overlooks this flaw, yet it happens to incur no bad effects on the correctness of MTO, even though the start point of this formulation is highly questionable. It is also this neglect that made MTO consider a variant of multi-bit DPA attack, which was mistakenly thought to appropriately serve as an alternative powerful attack. This implies the soundness of MTO is also more or less arguable. Therefore, we fix this definitional flaw and provide a revised definition named reVisited TO (VTO). For demonstrating validity and soundness of VTO, we present simulated and practical DPA attacks on implementations of $4\times 4$ and $8\times 8$ S-boxes. In addition, we also illustrate the soundness of VTO in masked S-boxes. Furthermore, as a concrete application of VTO, we present the distribution of VTO values of optimal affine equivalence classes of $4\times 4$ S-boxes and give some recommended guidelines on how to select $4\times 4$ S-boxes with higher DPA resistance at the identical level of implementation cost.

Abstract The advent of CUDA-enabled GPU makes it possible to provide cloud applications with high-performance data security services. Unfortunately, recent studies have shown that GPU-based applications are also susceptible to side-channel attacks. These published work studied the side-channel vulnerabilities of GPU-based AES implementations by taking the advantage of the cache sharing among multiple threads or high parallelism of GPUs. Therefore, for GPU-based bitsliced cryptographic implementations, which are immune to the cache-based attacks referred to above, only a power analysis method based on the high-parallelism of GPUs may be effective. However, the leakage model used in the power analysis is not efficient at all in practice. In light of this, we investigate electro-magnetic (EM) side-channel vulnerabilities of a GPU-based bitsliced AES implementation from the perspective of bit-level parallelism and thread-level parallelism in order to make the best of the localization effect of EM leakage with parallelism. Specifically, we propose efficient multi-bit and multi-thread combinational analysis techniques based on the intrinsic properties of bitsliced ciphers and the effect of multi-thread parallelism of GPUs, respectively. The experimental result shows that the proposed combinational analysis methods perform better than non-combinational and intuitive ones. Our research suggests that multi-thread leakages can be used to improve attacks if the multi-thread leakages are not synchronous in the time domain.

Low Entropy Masking Schemes (LEMS) have attracted wide attention due to their implementations simplicity and relatively good performance in protecting cryptographic implementations against Side-Channel-Attacks (SCAs). To achieve desired security, it is necessary (but not sufficient) to find proper low entropy mask sets to protect all sensitive secret-dependant intermediate variables. However, one crucial problem concerning this intuitive idea is that what `proper' mask sets should be. To formally capture such crucial qualification, we introduce the notion of balancedness to characterize this natural attribute of mask sets themselves. Considering that this notion is limited to characterize first-order security, we generalize it to d-dimension balancedness to accommodate dth-order security, then we exhibit lower and upper bounds on d-dimension balancedness for any d. With the help of these essential definitions, we prove that no balanced low entropy mask set really exists, which implies that LEMS implementations always have vulnerabilities in theory due to the unbalancedness of underlying mask sets. In order to further demonstrate the practical implications of balancedness, we show 4 different kinds of attacks on three state-of-the-art LEMS implementations. Specifically, the distribution attack proposed in this paper is a general first-order attack on LEMS. The results demonstrate that unbalanced mask sets actually do lead to serious vulnerabilities.

Many real world attacks often target the implementation of a cryptographic scheme, rather than the algorithm itself, and a system designer has to consider new models that can capture these attacks. For example, if the key can be tampered by physical attacks on the device, the security of the scheme becomes totally unclear. In this work, we investigate predicate encryption (PE), a powerful encryption primitive, in the setting of tampering attacks. First, we show that many existing frameworks to construct PE are vulnerable to tampering attacks. Then we present a new security notion to capture such attacks. Finally, we take Attrapadung’s framework in Eurocrypt’14 as an example to show how to “compile" these frameworks to tampering resilient ones. Moreover, our method is compatible with the original pair encoding schemes without introducing any redundancy.

In this work, for the first time, we investigate Electro-Magnetic (EM) attacks on GPU-based AES implementation. In detail, we first sample EM traces using a delicate trigger; then, we build a heuristic leakage model and a novel leakage model to exploit the simultaneous EM leakages in parallel scenarios. After that, we evaluate the effectiveness of EM attacks on GPU-based AES implementation. Our evaluation results show that GPU-based AES implementation is vulnerable to EM attacks. This work also suggests that GPU-based AES implementation needs to be protected against EM attacks in real scenarios.

Efficient cryptographic implementations with desired side-channel attacks (SCA) resistance are highly required, especially for those resources-constrained devices. In this paper, we propose a very compact AES hardware implementation scheme provably secure against 1st-order SCAs. Basically, our scheme is inspired by ideas of Redundant Tower Field (RTF for short) circuit due to Ueno et al. and of private circuits due to Ishai, Sahai and Wagner (ISW for short), and is therefore named ISW-RTF. In terms of security, practical attacks on real leakages from prototype implementation show that ISW-RTF scheme is secure against 1st-order attacks and 2nd-order zero-offset attacks as well. Results of t-test leakage detection of these leakages also verify this observation. In terms of efficiency, compared with the state-of-the-art 1st-order masking scheme, our scheme outperforms at least 55.08% decreases in area, and 34.87% decreases in area-time product on three popular FPGA/ASIC devices. To the best of our knowledge, the proposed ISW-RTF scheme is the most compact one provably secure against SCA.

Higher order side channel attacks (HOSCAs) exploit the side channel leakages of a masked crypto device at multiple leakage samples to recover the secret key used by the target crypto device. The attack price of HOSCA increases exponentially with the attack order, and HOSCA becomes infeasible when the attack order is high. Therefore, it is utmost important to employ the higher order optimal distinguishers (HOODs) to effectively decrease the attack price of HOSCA and make it applicable in a wide scenario. Recently, under the assumption that noises at different leakage samples are independent and that the noise at a single leakage sample follow the Gaussian distribution, Bruneau et al. proposed one HOOD. However, the two assumptions made by Bruneau et al. do not fit in with the real cases well. In light of this, the HOOD proposed by Bruneau et al. cannot be strictly speaking the optimal. Therefore, in this paper we propose the mahalanobis distance similarity measure (MDSM)-based HOOD. In the MDSM-based HOOD, no unsuitable assumptions are made. Therefore, the key-recovery efficiency of the MDSM-based HOOD should be higher than that of the maximum likelihood principle-based HOOD. In fact, both empirical and real evaluations are performed to support our point.

Side channel attacks (SCAs) are a kind of the most powerful means to evaluate the physical security of a crypto device. Multi-channel fusion attacks (MCFAs) belong in SCAs and utilize multi-channel leakages simultaneously. As compared with the known mono-channel attacks, MCFAs have higher potential leakage utilization. However, previous research about MCFAs is scarce. MCFAs have not been studied systematically and classified explicitly. It is hard to select MCFAs strategies properly and perform MCFAs efficiently according to the existing research. In light of this, we classify MCFAs into three groups from the view of fusion for the first time, including data-level, feature-level, and decision-level fusion attacks. Accordingly, we construct six MCFAs and verify their effectiveness in typical scenarios. The applicable scopes and the different performances of MCFAs with different fusion activities are also first discussed. To the best of our knowledge, this paper is the first to systematically investigate MCFAs. We hope that this paper will be helpful to understand MCFAs and offer advice on MCFAs against the different implementations of a crypto algorithm. Besides, a fusion metric that determines which channels are suitable for combination is also proposed and verified by practical experiments.

Most cryptographic implementations are vulnerable to side-channel attacks. Among the countermeasures, masking is the most popular one. In the field of provable secure masking schemes, it is a tough task to design a masking scheme for the multiplication with related multiplicands. Among all the corresponding solutions, the one proposed by Coronet al.in FSE 2013 achieves the best efficiency. Furthermore, in CRYPTO 2015, we claim that this scheme can be extended to secure any quadratic functions and therefore enables secure evaluation of S-box, which significantly outperforms any other methods. In this paper, we further promote the efficiency of Coron’s scheme by decreasing the random generations according to two observations. First, by modifying each pair of intermediate values on two sides of a diagonal line, half of all the required random values can be removed. Second, some randomness can be reused. All random values in one out of two lines can be replaced with the same random value. According to these two observations, we propose two new schemes. The first proposal improves the original scheme with a 50% randomness reduction and satisfies a stronger compositional security notion${d}$-SNI, while the second proposal improves the original scheme with a 50%–75% randomness reduction and satisfies a weaker compositional security notion${d}$-TNI. We give the security proof for both schemes. Moreover, we give an example of the masked Advanced Encryption Standard (AES) inversion circuits where both the first and second proposals are applied, significantly outperforming the original AES inversion with a 43%–57% saving of random generations. This indicates that our proposals can be used to build secure and efficient implementations of cryptographic algorithms.

Evaluating the success rate of a side-channel attack in design phase is of great significance for the design of secure crypto device. The reason is evaluation in design phase can help find and fix security vulnerabilities early. Existing methods of success rate evaluation in design phase ignore the preprocessing of real attacks. This results in a big gap between the success rate evaluated in design phase and that got in practical attacks. In this paper we propose a Hilbert Transform based vertical preprocessing, aiming at enhancing the signal-to-noise ratio. Compared to existing preprocessing methods aiming at a similar purposes, it can be mounted in design phase, which makes it possible to refine evaluation in design phase. Furthermore, the Hilbert Transform based vertical preprocessing can be used in real attacks as well, and its working efficiency is higher than that of other advanced preprocessing. We perform the vertical preprocessing not only in simulated scenarios but also in practical scenarios. Evaluation results confirm that vertical preprocessing leads to significant improvement of success rate. Therefore, we note vertical preprocessing can be an important tool for evaluating and analyzing of the practical security of crypto device.

Abstract. Under the assumption that one has a reference device iden-tical or similar to the target device, and thus be well capable of char-acterizing power leakages of the target device, Template Attacks are widely accepted to be the most powerful side-channel attacks. However, the question of whether Template Attacks are really optimal in terms of the leakage exploitation rate is still unclear. In this paper, we present a negative answer to this crucial question by introducing a normalization process into classical Template Attacks. Specifically, our contributions are two folds. On the theoretical side, we prove that Normalized Tem-plate Attacks are better in terms of the leakage exploitation rate than Template Attacks; on the practical side, we evaluate the key-recovery efficiency of Normalized Template Attacks and Template Attacks in the same attacking scenario. Evaluation results show that, compared with Template Attacks, Normalized Template Attacks are more effective. We note that, the computational price of the normalization process is of ex-tremely low, and thus it is very easy-to-implement in practice. Therefore, the normalization process should be integrated into Template Attacks as a necessary step, so that one can better understand practical threats of

Abstract Continual memory attacks, inspired by recent realistic physical attacks, have broken many cryptographic schemes that were considered secure in traditional cryptography model. In this paper, we consider the continual memory leakage resilience in public‐key encryption with keyword search scheme (PEKS). We give the definition of continual memory leakage resilience security for PEKS, which allows continual secret key leakage in the trapdoor generation algorithm rather than leakage of trapdoor itself. We believe that the definition is more suitable for practical PEKS scenario. To construct a concrete PEKS scheme secure against continual memory attacks, we firstly obtain a continual master‐key leakage‐resilient anonymous identity‐based encryption (IBE) scheme by applying the generic tool provided by Lewko et al. to a fully secure anonymous IBE scheme that comes from the fully secure anonymous hierarchical identity‐based encryption (HIBE) scheme of De Caro and colleagues. Then, we transform our continual master‐key leakage‐resilient anonymous IBE scheme to a PEKS scheme using the generic Anonymous IBE‐to‐PEKS transformation and prove its continual leakage‐resilient security. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In side‐channel attacks, information about any intermediate data except the target data will be considered as noise, namely, the “algorithmic noise.” Algorithmic noise is always assumed to be independent of the target data. As a result, it is thought to be an equivalence of physical noise. Numerous investigations are built on this intuitive assumption. In this paper, we claim that this assumption is not always true and find the algorithm (e.g., SIMON) for which the algorithmic noise is dependent on the target data. On this condition, we reconsider the issue of success rate estimation and build a modified statistical model to accurately estimate the success rate when the algorithmic noise is dependent on the target data. Finally, experimental results for SIMON algorithm validate the soundness of our methodology. It is also worth mentioning that SIMON is a candidate proposed by the National Security Agency as the standard light‐weight block cipher. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract There is more than one sample in a power trace, corresponding to the process of the target intermediate value, which are known as interesting points. One can exploit the power leakages at multiple samples to recover the secret key used by the crypto device, and that is the idea of multiple samples correlation power analysis (MSCPA). Considering that the leakage exploitation of MSCPA is more efficient than that of correlation power analysis (CPA), the power of MSCPA should be stronger than that of CPA. We theoretically prove and practically verify this fact. Theoretically, we show the advantage of MSCPA over CPA in terms of the correct key distinguishing level. We show that compared with CPA, MSCPA enlarges the gap between the correlation coefficient computed under the correct key and the correlation coefficients computed under wrong key guesses, which makes the correct key easier to be distinguished. Practically, we evaluate the performance of MSCPA in real scenarios. Using power traces provided by DPA Contest v2 and v4.1, we compare the key‐recovery efficiency of MSCPA with that of CPA. Experimental results show that compared with CPA, MSCPA can reach a higher key‐recovery efficiency in the same scenario. Copyright © 2016 John Wiley & Sons, Ltd.

Side Channel Attack (SCA) recovers secret information from an embedded device with implementation of cryptographic algorithm by exploiting its physical leakages. For most SCA methods to achieve good performance, the measured leakages are often desired to be well aligned. However, due to some specific reasons such as inaccurate measurements or carefully designed countermeasures, misalignment of leakages frequently occurs in practice. Misalignment significantly reduces the efficiency of SCA methods, or even makes them fail. To address this issue, two alignment approaches are proposed: a local alignment based onshotgun distanceand a global alignment based onweighted edit distance. Compared with previous methods, the proposed methods are capable of keeping the secret dependant leakages, while not introducing any redundant information. In addition, the proposed methods could also reduce the negative effects of noise, which is another factor seriously decreasing the efficiency of SCA methods. Interestingly, it is pretty easy to set appropriate parameters for these two methods. Practical experiments show that the proposed methods outperform previous methods in three different circumstances and different noise levels.

To tackle with physical attacks to real world cryptosystems, leakage resilient cryptography was developed. In this setting, the adversary is allowed to have access to the internal state of a cryptographic system, thus violates the black-box reduction used in cryptography. Especially when considering continual memory leakage (CML), i.e., there is no predetermined bound on the leakage of the internal information, the task is extremely tough. In this paper, we solve this problem by introducing a new primitive called updatable hash proof system (UHPS). A UHPS can be viewed as a special Hash proof system (HPS), which served as a fundamental tool in constructing public key encryption (PKE) schemes in both leakage-free and leaky settings. A remarkable property of UHPS is that by simply substituting the HPS component with a UHPS component in a PKE scheme, one obtains a new PKE scheme secure in the CML setting. Moreover, the resulting PKE scheme enjoys the same advantage of the original HPS-based PKE, for instance, still “compatible” with known transforms [ 8 , 20 , 24 , 32 ]. We then give instantiations of UHPS from widely-accepted assumptions, including the symmetric external Diffie-Hellman assumption and the d-linear assumption. Interestingly, we notice that when instantiated with concrete assumptions, the resulting chosen-ciphertext secure PKE scheme is by far the most efficient.

MPC-KSA, a generic side-channel distinguisher based on Kolmogorov-Smirnov Test, turns out to be a promising alternative to its original counterpart MIA. However, current implementations of MPC-KSA are undesirably slow in practice. In light of this, we present a fast implementation for MPC-KSA distinguisher. The main idea of the proposed method is based on an essential observation that one of the two samples used in Kolmogorov-Smirnov Test for MPC-KSA is always a true subset of the other. Using this inherent relationship between these two leakage samples, we reduce sorting times and accelerate the counting operations in Kolmogorov-Smirnov Test. In this way, the time complexity of Kolmogorov-Smirnov Test for MPC-KSA is reduced from O(n^2) to O(n), and that of MPC-KSA is reduced from O(n^2) to O(n log n). Besides this, we present another optimization technique that further improves the performance of MPC-KSA. In order to verify the efficiency of the proposed method, we have performed MPC-KSA against real power traces published in DPA Contest V2. The result shows that an attack against 15,000 traces takes only 3.4 ms per trace by our optimized method while 85.5 ms per trace by the naive method.

Abstract Under the assumption that power leakages at different interesting points follow multivariate normal distribution , maximum likelihood principle (MLP) can be used as an efficient distinguisher for template attack (TA). Therefore, in key‐recovery, one uses MLP to recover the correct key. In pattern recognition, Mahalanobis distance similarity measure (MDSM) is usually used to measure the similarity of two vectors in terms of their distance. A merit of MDSM is that, when measuring the similarity of two vectors, one takes the cross correlation between different variables into consideration. In this paper, we investigate the application of MDSM as a distinguisher in TA. We will show that there exists a certain relationship between MLP‐based TA and MDSM‐based TA under the assumption that the covariance matrices of different templates are identical . However, in MDSM‐based TA, power leakages at different interesting points are not required to follow multivariate normal distribution . We perform practical experiments to evaluate the key‐recovery efficiency of MDSM‐based TA. Experimental results verify that, in the same attack scenario, the key‐recovery efficiency of MDSM‐based TA can be higher than that of MLP‐based TA. Copyright © 2014 John Wiley & Sons, Ltd.

Private Information Retrieval (PIR) protocols allow users to learn data items stored at a server which is not fully trusted, without disclosing to the server the particular data element retrieved. Several PIR protocols have been proposed, which provide strong guarantees on user privacy. Nevertheless, in many application scenarios it is important to protect the database as well. In this paper, we investigate the amount of data disclosed by the the most prominent PIR protocols during a single run. We show that a malicious user can stage attacks that allow an excessive amount of data to be retrieved from the server. Furthermore, this vulnerability can be exploited even if the client follows the legitimate steps of the PIR protocol, hence the malicious request can not be detected and rejected by the server. We devise mechanisms that limit the PIR disclosure to a single data item.

to date, most of many-core prototypes employ tiled topologies connected through on-chip networks. The throughput and latency of the on-chip networks usually become to the bottleneck to achieve peak performance especially for communication intensive applications. Most of studies are focus on on-chip networks only, such as routing algorithms or router micro-architecture, to improve the above metrics. The salient aspect of our approach is that we provide a data management framework to implement high efficient on-chip traffic based on overall many-core system. The major contributions of this paper include that: (1) providing a novel tiled many-core architecture which supports software controlled on-chip data storage and movement management; (2) identifying that the asynchronous bulk data transfer mechanism is an effective method to tolerant the latency of 2-D mesh on-chip networks. At last, we evaluate the 1-D FFT algorithm on the framework and the performance achieves 47.6 Gflops with 24.8% computation efficiency.