Reviewerly/exHarmony huggingface dataset:
Performance Impact of PQC KEMs on TLS 1.3 Under Varying Network Characteristics

Widely used asymmetric primitives such as RSA or Elliptic Curve Diffie Hellman (ECDH), which enable authentication and key exchange, could be broken by Quantum Computers (QCs) in the coming years. Quantum-safe alternatives are urgently needed. However, a thorough investigation of these schemes is crucial to achieve sufficient levels of security, performance, and integrability in different application contexts. The integration into Transport Layer Security (TLS) plays an important role, as this security protocol is used in about 90% of today’s Internet connections and relies heavily on asymmetric cryptography. In this work, we evaluate different Post Quantum Cryptography (PQC) key establishment schemes in TLS 1.3 by extending the framework of Paquin et al.. We analyze the TLS handshake performance under variation of network parameters such as packet loss. This allows us to investigate the suitability of PQC KEMs in specific application contexts. We observe that Kyber and other structured lattice-based algorithms achieve very good overall performance and partially beat classical schemes. Other approaches such as FrodoKEM, HQC and BIKE show individual disadvantages. For these algorithms, there is a clear performance decrease when increasing the security level or using a hybrid implementation, e.g., a combination with ECDH. This is especially true for FrodoKEM, which, however, meets high security requirements in general. It becomes clear that performance is strongly influenced by the underlying network processes, which must be taken into account when selecting PQC algorithms.
Fregata: Faster Homomorphic Evaluation of AES via TFHE

Gentry et al. [26] first presented a homomorphic evaluation of the AES-128 based on the BGV scheme, however, it suffered from high evaluation latency. Despite considerable efforts have been directed towards designing FHE-friendly symmetric encryption algorithms, the efficient homomorphic evaluation of the well-studied and standardized AES remains an attractive challenge for researchers in the transciphering community. In this paper, we present a novel homomorphic evaluation framework based on the TFHE scheme, demonstrating the optimal latency for AES-128 evaluation. Specifically, we propose mixed packing to achieve efficient S-box evaluation and an optimized circuit bootstrapping as a bridge to connect the whole evaluation framework. Furthermore, we show the versatility of our evaluation framework by extending it to other ciphers, such as SM4. To validate the effectiveness of our proposed framework, we conduct implementation experiments, which indicate that the evaluation of AES takes 86 s on a single core, a 3 $$\times $$ improvement over the state-of-the-art [39]. Moreover, with a 16-thread parallel implementation, it takes about 9 s. For SM4 evaluation, it takes only 78 s on a single core, about 73 $$\times $$ improvement compared to publicly available BGV-based solution [40].
Symmetry Defense Against CNN Adversarial Perturbation Attacks

This paper uses symmetry to make Convolutional Neural Network classifiers (CNNs) robust against adversarial perturbation attacks. Such attacks add perturbation to original images to generate adversarial images that fool classifiers such as road sign classifiers of autonomous vehicles. Although symmetry is a pervasive aspect of the natural world, CNNs are unable to handle symmetry well. For example, a CNN can classify an image differently from its mirror image. For an adversarial image that misclassifies with a wrong label $$l_w$$ , CNN inability to handle symmetry means that a symmetric adversarial image can classify differently from the wrong label $$l_w$$ . Further than that, we find that the classification of a symmetric adversarial image reverts to the correct label. To classify an image when adversaries are unaware of the defense, we apply symmetry to the image and use the classification label of the symmetric image. To classify an image when adversaries are aware of the defense, we use mirror symmetry and pixel inversion symmetry to form a symmetry group. We apply all the group symmetries to the image and decide on the output label based on the agreement of any two of the classification labels of the symmetry images. Adaptive attacks fail because they need to rely on loss functions that use conflicting CNN output values for symmetric images. Without attack knowledge, the proposed symmetry defense succeeds against both gradient-based and random-search attacks, with up to near-default accuracies for ImageNet. The defense even improves the classification accuracy of original images.
Estimation of Hourly Solar Radiation in Australia Using Machine Learning Models

The prediction of global hourly solar radiation is of great significance for the development of solar energy resources in energy conversion countries. Based on this view, this study used RF and SVM models to simulate solar radiation data distributed at three weather stations in Australia based on different input combinations. In the training of the model, the meteorological data included in the parameter combination selected in this study include average temperature, relative humidity, extraterrestrial radiation and precipitation. The results showed that the simulation accuracy of SVM model was better than that of RF model, and the average RMSE was 0.61 and 0.68 MJ m−2 h−1, respectively. The model with Z2 input was significantly better than the model with Z1 input. The RMSE was reduced by 25% and the R2 was increased by 10%. Temperature had an important influence on solar radiation.
Impossible Differential Cryptanalysis of the FBC Block Cipher

The FBC block cipher is an award-winning algorithm of the recent Cryptographic Algorithm Design Competition in China. It employs a generalised Feistel structure and has three versions FBC128-128, FBC128-256 and FBC256, which have a 128-bit block size with a 128- or 256-bit user key and a 256-bit block size with a 256-bit user key, respectively. The best previously published cryptanalysis results on FBC are Zhang et al.’s impossible differential attack on 13-round FBC128-128 and Ren et al.’s boomerang attack on 13-round FBC128-256. In this paper, we observe that when conducting impossible differential cryptanalysis of FBC, both inactive and active nibble differences on plaintext and ciphertext as well as a few intermediate states may be exploited for some refined sorting conditions on plaintexts and ciphertexts to filter out preliminary satisfying plaintext/ciphertext pairs efficiently. Taking advantage of this observation, we use Zhang et al.’s 9-round impossible differentials of FBC128 to make key-recovery attacks on 14-round FBC128-128 and 15-round FBC128-256, and similarly we exploit 13-round impossible differentials on FBC256 and make a key-recovery attack on 19-round FBC256. Our results are better than any previously published cryptanalytic results on FBC in terms of the numbers of attacked rounds.
Loss and Likelihood Based Membership Inference of Diffusion Models

Recent years have witnessed the tremendous success of diffusion models in data synthesis. However, when diffusion models are applied to sensitive data, they also give rise to severe privacy concerns. In this paper, we present a comprehensive study about membership inference attacks against diffusion models, which aims to infer whether a sample was used to train the model. Two attack methods are proposed, namely loss-based and likelihood-based attacks. Our attack methods are evaluated on several state-of-the-art diffusion models, over different datasets in relation to privacy-sensitive data. Extensive experimental evaluations reveal the relationship between membership leakages and generative mechanisms of diffusion models. Furthermore, we exhaustively investigate various factors which can affect membership inference. Finally, we evaluate the membership risks of diffusion models trained with differential privacy.
Withdrawable Signature: How to Call Off a Signature

Digital signatures are a cornerstone of security and trust in cryptography, providing authenticity, integrity, and non-repudiation. Despite their benefits, traditional digital signature schemes suffer from inherent immutability, offering no provision for a signer to retract a previously issued signature. This paper introduces the concept of a withdrawable signature scheme, which allows for the retraction of a signature without revealing the signer’s private key or compromising the security of other signatures the signer created before. This property, defined as “withdrawability”, is particularly relevant in decentralized systems, such as e-voting, blockchain-based smart contracts, and escrow services, where signers may wish to revoke or alter their commitment. The core idea of our construction of a withdrawable signature scheme is to ensure that the parties with a withdrawable signature are not convinced whether the signer signed a specific message. This ability to generate a signature while preventing validity from being verified is a fundamental requirement of our scheme, epitomizing the property of withdrawability. After formally defining security notions for withdrawable signatures, we present two constructions of the scheme based on the pairing and the discrete logarithm. We provide proofs that both constructions are unforgeable under insider corruption and satisfy the criteria of withdrawability. We anticipate our new type of signature will significantly enhance flexibility and security in digital transactions and communications.
Function-Hiding Zero Predicate Inner Product Functional Encryption from Pairings

In this work, we investigate the possibility of constructing function-hiding predicate functional encryption (FH-P-FE), which provides full attribute-hiding and function-hiding securities. Concretely, the security of FH-P-FE guarantees that (1), the functional secret keys hide the underlying functions and (2), the decryption works with an access control as in predicate encryption (PE). Our results show that the new paradigm is achievable. To achieve this goal, we first give a formal definition of FH-P-FE and its security model under an indistinguishability-based notion. Then we construct a secret-key function-hiding zero predicate inner product functional encryption (FH-ZP-IPFE) from pairings. In a FH-ZP-IPFE, both secret keys and ciphertexts are associated with vectors. Given a secret key for a policy $$\boldsymbol{x}\in \mathbb {Z}_q^n$$ and a function $$\boldsymbol{u}\in \mathbb {Z}_q^m$$ , and a ciphertext for an attribute $$\boldsymbol{y}\in \mathbb {Z}_q^n$$ and a message $$\boldsymbol{v}\in \mathbb {Z}_q^m$$ , a decryptor learns the inner product value $$\langle \boldsymbol{u},\boldsymbol{v}\rangle $$ if and only if $$\langle \boldsymbol{x},\boldsymbol{y}\rangle =0\mod q$$ . Our construction is efficient, where the functional secret key and ciphertext both contain $$(n+m+1)$$ group elements. We prove the security in the generic group model of bilinear groups.
Protecting Private Keys of Dilithium Using Hardware Transactional Memory

The confidentiality of cryptography keys is necessary in cryptographic implementations. In order to resist memory disclosure attacks that steal sensitive variables such as private keys, various schemes are proposed to implement RSA and ECDSA. Meanwhile, with the migration towards post-quantum cryptography, Dilithium has been considered as the most potential signature algorithm. It involves more and larger sensitive variables, so that existing solutions for traditional cryptographic primitives are unapplicable. In this paper, we employ hardware transactional memory (HTM) to construct the secure environment for Dilithium signing operations, protecting private keys and all other sensitive variables against memory disclosure attacks. Based on the comprehensive sensitivity analysis of variables in the Dilithium algorithm, we restrict the whole sensitive operations in transactional execution regions and adopt the transaction-splitting technology for efficiency. We implemented the prototype using Intel TSX, demonstrating its security against memory disclosure attacks and acceptable performance overheads. For example, the security-enhancement implementation of Dilithium3, which is recommended by NIST, achieves a factor of 0.75 compared to the throughput of reference implementations.
Towards Explainability of the Latent Space by Disentangled Representation Learning

Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community.
Nature inspired-based remora optimisation algorithm for enhancement of density peak clustering

Density peak clustering (DPC) has shown promising results for many complex problems when compared with other existing clustering techniques. Inspite of many advantages, DPC suffers with lack of cluster centroids and cut-off distance identification. Cut-off distance is the prominent parameter used in the calculation of local density. The improper choice of cut-off distance leads to improper cluster results. Currently, the cut-off distance is selected using decision graph or delta density or knee point detection or silhouette score or kernel functions. The main problem with the above functions for selecting the cut-off distance in DPC is that they often rely on heuristic or visually subjective criteria, making the choice of the optimal cut-off distance challenging and potentially sensitive to data characteristics. By leveraging metaheuristic optimisation algorithms, the process of selecting the cut-off distance becomes less subjective and data-driven, potentially leading to improved clustering results in DPC. This motivated us to work on the choice of cut-off distance by the usage of remora optimisation algorithm (ROA). The cluster results are improved by the usage of remora in selection of reliable cut-off distance (dc). The effectiveness of the updated DPC with ROA is evaluated by applying on the eight datasets and compared with K-means, traditional DPC, DPC merged with other optimisation results. The three parameters used here to check the quality of the cluster are homogeneity, completeness and silhouette analysis. ROA is new and built on the inspiration of remora which moves from one place to another using the sea fishes like shark, whale, sword fish, etc. It is clear from the results that DPC with ROA has produced the better homogeneity value of 0.807, completeness of 0.699 and silhouette analysis of 0.79 than the other clustering algorithms.
Color Transfer for Images: A Survey

High-quality image generation is an important topic in digital visualization. As a sub-topic of the research, color transfer is to produce high-quality image with ideal color scheme learned from the reference one. In this paper, we investigate the mainstream methods of color transfer to provide a survey which introduces the related theories and frameworks. Such methods can be concluded into three categories: statistical color transfer; semantic-based color transfer; color transfer for special target. For these mainstream technical routes, we discuss the related research background, technical details, and representative methods. We also exhibit some new trends of the topic according to the recent progress. Based on the comparisons, we discuss the unsolved issues of color transfer and potential solutions in future work.
Multi-Object Tracking with Spatial-Temporal Tracklet Association

Recently, the tracking-by-detection methods have achieved excellent performance in Multi-Object Tracking (MOT), which focuses on obtaining a robust feature for each object and generating tracklets based on feature similarity. However, they are confronted with two issues: 1) unstable features in short-term occlusion; 2) insufficient matching in long-term occlusion. Specifically, the unstable feature is caused by the appearance variation under occlusion, and the association with the current unstable feature will lead to insufficient matching in long-term occlusion. To address the above issues, we propose a two-stage tracklet-level association method, Spatial-Temporal Tracklet Association (STTA), to effectively combine spatial-temporal context between feature extraction and data association. In the first stage, we propose the Tracklet-guided Spatial-Temporal Attention network (TSTA) to generate robust and stable features. Specifically, TSTA captures spatial-temporal context to obtain the most salient regions between the current and previous clips. In the second stage, we design the Bi-Tracklet Spatial-Temporal association (BTST) module to fully exploit the spatial-temporal context in data association. Specifically, we leverage BTST to merge different tracklets into long-term trajectories by jointly learning visual feature and spatial-temporal context, and designing a bidirectional interpolation to recover the missed objects between matched tracklets. Extensive experiments of public and private detections on four benchmarks demonstrate the robustness of STTA. Furthermore, the proposed method is a model-agnostic method, which can be plugged and played with existing methods to boost their performance, e.g. , obtain 11.0%, 10.1%, 2.9%, 3.2%, 7.8% improvement on IDF1 in MOT16 validation dataset for Tracktor, CenterTrack, Deepsort, JDE, and CTracker, respectively.
Rethinking ICT4D

This chapter proposes a slightly modified framework for policymakers and academics when using information technology as a means of addressing development problems, introducing a series of questions that should lead policymakers to decide if, how, and when information technology interventions are appropriate in a given context, and, if so, how to design, test, and evaluate them. It points out some of the “pitfalls” related to using digital for improving development outcomes in sub-Saharan Africa.
An efficient hexadecimal network flow watermark method for tracking attack traffic

Abstract Network flow watermark technology is a traffic marking technique that embeds watermark information into the characteristics of network flows to mark and trace attack flows generated by network attackers. However, with the development of network attack techniques, the time and number of packets required for network attacks have decreased. Existing network flow watermark technologies fail to balance watermark robustness and efficiency, resulting in poor practicality. To address this issue, this paper proposes an efficient hexadecimal network flow watermark method. The method introduces an efficient interval watermark algorithm and utilizes an interval synchronization algorithm to self-learn watermark parameters, thereby improving the encoding efficiency of the watermark. The design of watermark start and end markers ensures the practicality of network watermarks, enabling traceability and source attribution of attack flows in real network environments. The proposed method is experimentally tested using real network traffic, and the results demonstrate that even in the presence of a network jitter, the watermark detection success rate of this scheme remains above 95%. Compared to other network flow watermark schemes, the hexadecimal network flow watermark proposed in this paper achieves a 50% improvement in encoding and decoding efficiency while ensuring robustness. It also exhibits excellent resistance to network jitter, packet loss, and false packet insertion.
Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification

Designing deep learning (DL) models that adapt to new data without forgetting previously acquired knowledge is important in the medical field where data is generated daily, posing a challenge for model adaptation and knowledge retention. Continual learning (CL) enables models to learn continuously without forgetting, typically on a sequence of domains with known domain identities (e.g. source of data). In this work, we address a more challenging and practical CL scenario where information about the domain identity during training and inference is unavailable. We propose Continual-GEN, a novel forget-free, replay-free, and domain-agnostic subnetwork-based CL method for medical imaging with a focus on skin lesion classification. Continual-GEN proposes an ensemble of groups approach that decomposes the training data for each domain into groups of semantically similar clusters. Given two domains, Continual-GEN assesses the similarity between them based on the distance between their ensembles and assigns a separate subnetwork if the similarity score is low, otherwise updating the same subnetwork to learn both domains. At inference, Continual-GEN selects the best subnetwork using a distance-based metric for each test data, which is directly used for classification. Our quantitative experiments on four skin lesion datasets demonstrate the superiority of Continual-GEN over state-of-the-art CL methods, highlighting its potential for practical applications in medical imaging. Our code: https://github.com/nourhanb/Continual-GEN .
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data

Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data. However, the limited access to fully annotated training data poses a major challenge to training generalizable models. We propose “ConDistFL”, a framework to solve this problem by combining FL with knowledge distillation. Local models can extract the knowledge of unlabeled organs and tumors from partially annotated data from the global model with an adequately designed conditional probability representation. We validate our framework on four distinct partially annotated abdominal CT datasets from the MSD and KiTS19 challenges. The experimental results show that the proposed framework significantly outperforms FedAvg and FedOpt baselines. Moreover, the performance on an external test dataset demonstrates superior generalizability compared to models trained on each dataset separately. Our ablation study suggests that ConDistFL can perform well without frequent aggregation, reducing the communication cost of FL. Our implementation will be available at https://github.com/NVIDIA/NVFlare/tree/main/research/condist-fl .