arXiv subjects

Ahmed A. Abd El-Latif

Publications and source records attributed to Ahmed A. Abd El-Latif.

6 recordsLinked to original sources

arXiv2023

A Practical Multi-Protocol Collaborative QKD Networking Scheme

With the advancement of quantum computing, the security of public key cryptography is under serious threat. To guarantee security in the quantum era, Quantum Key Distribution has become a competitive solution. QKD networks can be classified into measurement-device-dependent network and measurement-device-independent network. In measurement-device-dependent networks, the information is available for all trusted relays. This means that all trusted relays are strongly trusted relays that require strict control, which is difficult to realize. To address this issue, measurement-device-independent networks reduce the proportion of strongly trusted relay nodes by introducing untrusted relays. However, due to the higher key rate of measurement-device-dependent protocols over short distances, the communication capability of measurement-device-independent networks has a degradation compared to measurement-device-dependent networks. Therefore, how to reduce the dependence of QKD networks on strong trusted relays without significantly affecting the communication capability has become a major issue in the practicalization process of QKD networks. To address this issue, a novel Multi-Protocol Collaborative networking cell is proposed in this paper. The QKD network built by the MPC networking cell reduces the dependence on strongly trusted relays by combining the two protocols to introduce weak trusted relays while maintaining the high communication capacity. What's more, to further enhance the overall performance of the QKD network, an optimal topology design method is presented via the proposed flow-based mathematical model and optimization method. The simulation results show that the proposed scheme reduces the dependence on strongly trusted relays without a significant reduction in communication capability, our work holds great significance in promoting the practicalization of QKD networks.

quant-ph↗

arXiv2023

A Remote Quantum Error-correcting Code Preparation Protocol on Cluster State

The blind quantum computation (BQC) protocol allows for privacy-preserving remote quantum computations. In this paper, we introduce a remote quantum error correction code preparation protocol for BQC using a cluster state and analyze its blindness in the measurement-based quantum computation model. Our protocol requires fewer quantum resources than previous methods, as it only needs weak coherent pulses, eliminating the need for quantum memory and limited quantum computing. The results of our theoretical analysis and simulations show that our protocol requires fewer quantum resources compared to non-coding methods with the same qubit error rate.

quant-ph↗

arXiv2022

An Efficient Routing Protocol for Quantum Key Distribution Networks

Quantum key distribution (QKD) can provide point-to-point information-theoretic secure key services for two connected users. In fact, the development of QKD networks needs more focus from the scientific community in order to broaden the service scale of QKD technology to deliver end-to-end secure key services. Of course, some recent efforts have been made to develop secure communication protocols based on QKD. However, due to the limited key generation capability of QKD devices, high quantum secure key utilization is the major concern for QKD networks. Since traditional routing techniques do not account for the state of quantum secure keys on links, applying them in QKD networks directly will result in underutilization of quantum secure keys. Therefore, an efficient routing protocol for QKD networks, especially for large-scale QKD networks, is desperately needed. In this study, an efficient routing protocol based on optimized link-state routing, namely QOLSR, is proposed for QKD networks. QOLSR considerably improves quantum key utilization in QKD networks through link-state awareness and path optimization. Simulation results demonstrate the validity and efficiency of the proposed QOLSR routing protocol. Most importantly, with the growth of communication traffic, the benefit becomes even more apparent.

quant-ph↗

arXiv2020

A Secure Federated Learning Framework for 5G Networks

Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

cs.CR↗

arXiv2019

Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities

. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. Physikalisch-technische bundesanstalt (PTB) Diagnostic ECG database is used for experimentation, which has been widely employed in MI detection studies. In case of using VGG-MI1, we achieved an accuracy, sensitivity, and specificity of 99.02%, 98.76%, and 99.17%, respectively and we achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49% with VGG-MI2 model. Experimental results validate the efficiency of the proposed system in terms of accuracy sensitivity, and specificity.

cs.LG↗

arXiv2019

Dominant Dataset Selection Algorithms for Electricity Consumption Time-Series Data Analysis Based on Affine Transformation

The explosive growth of time-series data, the scale of time-series data (TSD) suggests that the scale and capability of many Internet of Things (IoT)-based applications has already been exceeded. Moreover, redundancy persists in TSD due to the correlation between information acquired via different sources. In this paper, we propose a cohort of dominant dataset selection algorithms for electricity consumption time-series data with a focus on discriminating the dominant dataset that is small dataset but capable of representing the kernel information carried by time-series data with an arbitrarily small error rate less than ". Furthermore, we prove that the selection problem of the minimum dominant dataset is an NP-complete problem. The affine transformation model is introduced to define the linear correlation relationship between time-series data objects. Our proposed framework consists of the scanning selection algorithm with O(n3) time complexity and the greedy selection algorithm with O(n4) time complexity, which are respectively proposed to select the dominant dataset based on the linear correlation distance between time-series data objects. The proposed algorithms are evaluated on the real electricity consumption data Harbin city in China. The experimental results show that the proposed algorithms not only reduce the size of the extracted kernel dataset but also ensure the time-series data integrity in terms of accuracy and efficiency.

cs.DB↗