ECGSC: Elliptic Curve based Generalized Signcryption Scheme

Signcryption is a new cryptographic primitive that simultaneously fulfills both the functions of signature and encryption. The definition of generalized signcryption is proposed in the paper firstly. Generalized signcryption has a special feature that provides confidentiality or authenticity separately under the condition of specific inputs. So it is more useful than common ones. Based on ECDSA, a signcryption scheme called ECGSC is designed. It will be equivalent to an AtE(OTP$,MAC) encryption scheme or ECDSA when one of party is absent. A third party can verify the signcryption text publicly in the method of ECDSA. Security properties are proven based on Random Oracle mode: confidentiality (CUF-CPA), unforgeability (UF-CMA) and non-repudiation. Compared with the others, ECGSC presents a 78% reduction in computational cost for typical security parameters for high level security applications

Label-Noise Learning with Intrinsically Long-Tailed Data

Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth classes of the training data are balanced. However, the real-world data is often imbalanced, leading to the inconsistency between observed and intrinsic class distribution with label noises. In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution. In this paper, we propose a learning framework for label-noise learning with intrinsically long-tailed data. Specifically, we propose two-stage bi-dimensional sample selection (TABASCO) to better separate clean samples from noisy samples, especially for the tail classes. TABASCO consists of two new separation metrics that complement each other to compensate for the limitation of using a single metric in sample separation. Extensive experiments on benchmarks demonstrate the effectiveness of our method. Our code is available at https://github.com/Wakings/TABASCO.Comment: Accepted by ICCV 202

Improving the Performance of R17 Type-II Codebook with Deep Learning

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.Comment: Accepted by IEEE GLOBECOM 2023, conference version of Arxiv:2305.0808

On Security Notions for Verifiable Encrypted Signature

First we revisit three - BGLS, MBGLS and GZZ verifiably encrypted signature schemes[2,3,6].We find that they are all not strong unforgeable.We remark that the notion of existential unforgeable is not sufficient for fair exchange protocols in most circumstances.So we propose three new - NBGLS, MBGLS and NGZZ verifiably encrypted signature schemes which are strong unforgeable. Also we reconsider other two - ZSS and CA verifiably encrypted signature schemes[4,8], we find that they both cannot resist replacing public key attack. So we strongly suggest that strong unforgeable for verifiably encrypted signature maybe a better notion than existential unforgeable and checking adjudicator knowing its private key is a necessary step for secure verifiably encrypted signature scheme

Provable Secure Generalized Signcryption

Generalized signcryption which proposed by Han is a new cryptographic primitive which can work as an encryption scheme, a signature scheme or a signcryption scheme[5]. However,the security proof in their paper is not very formal.our contribution are as following:First we give security notions for this new primitive.Secnond,we give an attack to [4]which is the first vision of [5] and propose an improved generalized signcryption scheme. Third, we give new very formal proofs for this new scheme

Extend FHEW to General Case

When talking about FHE, refresh process is a little different from bootstrapping process. Bootstrapping always means that a scheme homomorphic decrypting its process, while refresh imply that use another scheme, always in large scale, to perform its decryption process. In EUROCRYPT’2015, Ducas and Micciancio proposed a FHE which can perform refresh process in less than a second, called DM14, while the scheme only support bite plaintext space, which is cumbersome for many applications. Extending DM14 to a large plaintext space becomes an open problem. In order to solve it, we improved the msbExtract process to endure a large base, by mapping the element to position. As a result, we constructed an efficient FHE with large plaintext space and quickly refresh process. We implemented our scheme in computer, and made a comparison between our performance and DM14. The result is that the running time is almost same, when extend the plaintext space from 2 to 8

Federated Learning with Extremely Noisy Clients via Negative Distillation

Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noise by a re-weighting strategy with a strong assumption, i.e., mild label noise. However, it may be violated in many real-world FL scenarios because of highly contaminated clients, resulting in extreme noise ratios, e.g., $>$90%. To tackle extremely noisy clients, we study the robustness of the re-weighting strategy, showing a pessimistic conclusion: minimizing the weight of clients trained over noisy data outperforms re-weighting strategies. To leverage models trained on noisy clients, we propose a novel approach, called negative distillation (FedNed). FedNed first identifies noisy clients and employs rather than discards the noisy clients in a knowledge distillation manner. In particular, clients identified as noisy ones are required to train models using noisy labels and pseudo-labels obtained by global models. The model trained on noisy labels serves as a `bad teacher' in knowledge distillation, aiming to decrease the risk of providing incorrect information. Meanwhile, the model trained on pseudo-labels is involved in model aggregation if not identified as a noisy client. Consequently, through pseudo-labeling, FedNed gradually increases the trustworthiness of models trained on noisy clients, while leveraging all clients for model aggregation through negative distillation. To verify the efficacy of FedNed, we conduct extensive experiments under various settings, demonstrating that FedNed can consistently outperform baselines and achieve state-of-the-art performance. Our code is available at https://github.com/linChen99/FedNed.Comment: Accepted by AAAI 202

Prediction of DNA i-motifs via machine learning

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine

Study on Noise Source Analysis and Control Method of Gas Station

With the rapid development of economy, environmental issues have attracted more and more attention from all walks of life. As a new type of efficient energy, urban gas plays a vital role in promoting the healthy development of cities and mitigating urban air pollution. As the hub of urban gas, gas stations play the role of gas distribution, peak regulation and pressure regulation, and are necessary facilities for the safe and stable operation of urban gas pipeline network. With the increase of the number of urban gas stations, the noise problem generated by the stations is becoming increasingly prominent, which seriously affects the quality of people’s life and social environment. Based on the analysis of the causes of gas station noise, this paper analyzes the noise control strategy of gas station

Efficient Multi-key FHE with short extended ciphertexts and less public parameters

Multi-Key Full Homomorphic Encryption (MKFHE) can perform arbitrary operations on encrypted data under different public keys (users), and the final ciphertext can be jointly decrypted by all involved users. Therefore, MKFHE has natural advantages and application value in security multi-party computation (MPC). The MKFHE scheme based on Brakerski-Gentry-Vaikuntanathan (BGV) inherits the advantages of BGV FHE scheme in aspects of encrypting a ring element, the ciphertext/plaintext ratio, and supporting the Chinese Remainder Theorem (CRT)-based ciphertexts packing technique. However some weaknesses also exist such as large ciphertexts and keys, and complicated process of generating evaluation keys. In this paper, we present an efficient BGV-type MKFHE scheme. Firstly, we construct a nested ciphertext extension for BGV and separable ciphertext extension for Gentry-Sahai-Waters (GSW), which can reduce the size of the extended ciphertexts about a half. Secondly, we apply the hybrid homomorphic multiplication between RBGV ciphertext and RGSW ciphertext to the generation process of evaluation keys, which can significantly reduce the amount of input/output ciphertexts and improve the efficiency. Finally, we construct a directed decryption protocol which allows the evaluated ciphertext to be decrypted by any target user, thereby enhancing the ability of data owner to control their own plaintext, and abolish the limitation in current MKFHE schemes that the evaluated ciphertext can only be decrypted by users involved in homomorphic evaluation