Encrypted Communication Research Articles

Anisotropic Coupling‐Based 2D Material Dember Polarized Photodetectors

AbstractPolarization photodetectors (PPDs) based on 2D materials hold great promise for ultracompact polarimeters due to their ability to directly detect polarization states. However, the existing PPDs based on 2D materials tend to focus on a single metric, making it challenging to obtain both a large polarization ratio (PR) and high responsivity (R). Here, black phosphorus (BP) PPDs are proposed under a nanowire configuration that enables large PR and high R simultaneously. Benefitting from the anisotropic coupling between the BP crystal and the nanowire geometry, specific light‐field distributions are formed under orthogonally polarized light incidences, through selectively exciting the localized surface plasmon and leaky‐mode resonances. A large carrier concentration gradient inherent to this structure is formed to induce a high response contrast and a large Dember current, enabling an ultrahigh PR ≈ 118.4, R ≈ 802.42 A W−1, and ultrashort response time ≈ 636 ps. Finally, a key‐free optical encryption communication system is constructed, demonstrating one of its promising applications.

Programmable metasurface based phase-modulating reflector for 2.4 GHz wireless communications

Abstract This paper presents an innovative programmable metasurface structure that achieves precise phase control within the 2 to 2.7 GHz frequency range by adjusting the state of varactor diodes embedded in the unit cells. The design employs a single diode, which simplifies the structure, reduces manufacturing costs, and minimizes reflection loss. At 2.4 GHz, the metasurface achieves 1-bit phase responses of 0° and 180°, with a reflection amplitude exceeding 0.72, demonstrating excellent reflective performance. Moreover, as the 2.4 GHz frequency is closely related to wireless communication bands, this programmable metasurface shows significant potential in the field of wireless communication encryption. By integrating dual varactor diodes, the design enables 2-bit phase control with reflection phase angles of 0°, 90°, 180°, and 270°. To validate the design, a 1-bit metasurface structure was fabricated and tested, with experimental results showing a high degree of consistency with simulations, highlighting the potential of this structure in enhancing wireless communication security.

Realizing Bidirectional Photocurrent in Monolithic Dual‐Mode Device for Neuromorphic Vision and Logically‐Encrypted Transmission

AbstractDue to significant response contradictions, unidirectional carrier transmission of typical semiconductor p‐n junctions limits integrated sensors and artificial synapses in a monolithic device. In this work, a bipolar p‐n junction designed by employing the hydrogel/p‐GaN local contact interface with p‐GaN/(In,Ga)N heterojunction, demonstrating the bidirectional photocurrent and sensor/artificial synapse dual‐mode device successfully. After modifying Au nanoparticles, the negative (positive) photocurrent is increased by 900% (300%) under 365 nm (520 nm) light to achieve a more balanced bipolar carrier dynamic. Such a regulation of photocurrent is found to be attributed to the promotion of hydrogen evolution reaction (HER) at the hydrogel/Au/p‐GaN interface. Thus, the device achieves the ultrahigh paired‐pulse facilitation (PPF) index of 243% under self‐powered condition. Moreover, the implementation of plasticity from short‐term to long‐term demonstrates its adaptability in neural morphology visual recognition. Beyond independent working mode, the function of five classic logic gates is achieved under three‐terminal operation. Finally, an encrypted wireless communication system using dual‐channel light signals demonstrates the extensive application potential of the monolithic device. Therefore, this work presents a viable construction blueprint to meet the demands of next‐generation all‐in‐one optoelectronics for intricate application scenarios.

Autonomous Vehicle Data Protection : A Review of Security Threats, Challenges and Protective Mechanism

The swift advancement of autonomous vehicle (AV) technology has presented notable obstacles with data security and privacy. This analysis examines major security threats that jeopardize user privacy and vehicle control, including data breaches, virus penetration, cyber attacks, illegal access, V2V and V2I system hacking, GPS spoofing, and data leaks. It also looks at the challenges developers have when putting strong security measures in place, considering how complicated AV infrastructures may be and how cloud, IoT, and AI are integrated. The paper outlines a number of defence tactics, such as blockchain technology, machine learning for anomaly detection, encryption, and secure communication protocols. In order to improve AV cyber security, it ends with suggestions for further study in fields like edge computing and quantum-resistant cryptography.

L-GraphSAGE: A Graph Neural Network-Based Approach for IoV Application Encrypted Traffic Identification

Recently, with a crucial role in developing smart transportation systems, the Internet of Vehicles (IoV), with all kinds of in-vehicle devices, has undergone significant advancement for autonomous driving, in-vehicle infotainment, etc. With the development of these IoV devices, the complexity and volume of in-vehicle data flows within information communication have increased dramatically. To adapt these changes to secure and smart transportation, encrypted communication realization, real-time decision-making, traffic management enhancement, and overall transportation efficiency improvement are essential. However, the security of a traffic system under encrypted communication is still inadequate, as attackers can identify in-vehicle devices through fingerprinting attacks, causing potential privacy breaches. Nevertheless, existing IoV traffic application models for encrypted traffic identification are weak and often exhibit poor generalization in some dynamic scenarios, where route switching and TCP congestion occur frequently. In this paper, we propose LineGraph-GraphSAGE (L-GraphSAGE), a graph neural network (GNN) model designed to improve the generalization ability of the IoV application of traffic identification in these dynamic scenarios. L-GraphSAGE utilizes node features, including text attributes, node context information, and node degree, to learn hyperparameters that can be transferred to unknown nodes. Our model demonstrates promising results in both UNSW Sydney public datasets and real-world environments. In public IoV datasets, we achieve an accuracy of 94.23%(↑0.23%). Furthermore, our model achieves an F1 change rate of 0.20%(↑96.92%) in α train, β infer, and 0.60%(↑75.00%) in β train, α infer when evaluated on a dataset consisting of five classes of data collected from real-world environments. These results highlight the effectiveness of our proposed approach in enhancing IoV application identification in dynamic network scenarios.

Bioinspired Three-Mode Photosensitive Synaptic LED for Optical Information Processing.

Inspired by human sense organs, AI is advancing toward multimodal perception, with display technology evolving into intelligent human-computer interaction tools. However, hardware networks with multimodal responses connected by different devices bring problems such as delayed information transfer and inefficiency. Thus, an innovative three-mode photosensitive synaptic LED (PSSL) is first proposed by adding a photosensitive layer indacenodithiophene-benzothiadiazole (IDTBT) to the quantum-dot light-emitting diode (QLED), switched by changing the bias voltage. The self-powered PSSL has a photoresponse range from 310 nm to 808 nm (ultraviolet-near-infrared, UV-NIR). The device exhibits a bipolar response under red and UV light at 1 V. When the voltage reaches the turn-on voltage, the PSSL device turns into a neuromorphic LED, exhibiting conductivity enhancement under red-light irradiation and suppression under UV-light irradiation. As a result, the PSSLs are expected to be applied in the field of optical encryption communication and in neuromorphic display.

Secure Data Aggregation and Transmission System for Wireless Body Area Networks Using Twofish Symmetric Key Generation

Nowadays, Wireless Body Area Networks (WBANs) are mostly used in the healthcare industry. They represent a portable, inexpensive network that exhibition adaptability. The data developed using WBAN devices is vulnerable to transmission-related internal and external attacks; nevertheless, this vulnerability arises due to resource restrictions; by employing data aggregation technologies to conduct statistical analyses of medical data while protecting patient privacy, medical professionals can enhance the precision of diagnoses and assist medical insurance firms in selecting optimal plans for their clients. Maintaining the confidentiality and integrity of sensitive health information becomes more stimulating at the stages of aggregation and transmission due to security issues. This study proposes a novel method, Twofish Symmetric Key Generation (TFSKG), combined into a Secure Data Aggregation (SDA) and transmission system intended for WBANs. The Twofish technique is animatedly employed to make the secure symmetric keys chosen for its robust encryption capabilities. These keys are used to encrypt and decrypt aggregated health data through transmission. The proposed TFSKG-SDA method implements effective algorithms for aggregating data to safeguard end-to-end privacy and preserve data accuracy while reducing bandwidth consumption. Thus, for improved performance, an innovative genetic algorithm for data security is presented in this study. This paper introduces TFSKG-SDA, a system that, by employing rigorous simulation testing, enhances security protocols, resistance against recognized threats, and data transmission efficacy in the context of resource-constrained WBANs. We assess the encryption strength, computational cost, and communication efficiency of the TFSKG- SDA method to prove its significance to real-world healthcare applications.

Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback

Unlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND and the fuzzy symbol OR as well as nonlinear activation behaviors are all concerned in the generalized master-slave networks. Based on the characteristics of the neural networks being studied, we have designed distinctive information feedback control protocols including three different functional sub-modules. Combining comparative theorems, inequality techniques, and stability theory, novel delay-independent conditions can be derived to ensure the finite-time synchronization (FTS) of fuzzy CFUMNNs. Besides, the upper bound of the settling time can be effectively evaluated based on feedback coefficients and control parameters, which makes the achievements of this study more practical for engineering applications such as signal encryption and secure communications. Ultimately, simulation experiments show the feasibility of the derived results.

Quantum Physical Unclonable Function Based on Multidimensional Fingerprint Features of Single Photon Emitters in Random AlN Nanocrystals

AbstractPhysical unclonable function (PUF) has emerged as a unique physical'fingerprint' that is inherently difficult to replicate. It shows tremendous application value in various hardware security areas such as identity authentication, chip anticounterfeiting, communication encryption, blockchain, etc. However, with the rapid development of 3D nanoprinting, classical PUFs constructed with disordered micro‐nanostructures face tremendous threats from physical cloning attacks. Herein, this study proposes and demonstrates the utilization of room‐temperature single‐photon emitters derived from atomic defects in randomly distributed pyramidal aluminum nitride (AlN) nanocrystals as a novel quantum PUF to resist physical cloning attacks. The fabrication of this quantum PUF on silicon (Si) wafers enables seamless integration with silicon photonic integrated circuits. The multidimensional fingerprint features of the single‐photon emitters are highly sensitive to the lattice parameters of the uneven AlN nanocrystals. Furthermore, each single photon emitter can work as a quantum random number generator to ensure the fundamental unpredictability of PUFs. The subatomic precision requirement coupled with unpredictable quantum emission behavior makes it practically impossible to attack the proposed quantum PUF, providing a promising solution for information security in the post‐quantum era.

Joint encryption and error correction for secure quantum communication

Secure quantum networks are a bedrock requirement for developing a future quantum internet. However, quantum channels are susceptible to channel noise that introduce errors in the transmitted data. The traditional approach to providing error correction typically encapsulates the message in an error correction code after encryption. Such separate processes incur overhead that must be avoided when possible. We, consequently, provide a single integrated process that allows for encryption as well as error correction. This is a first attempt to do so for secure quantum communication and combines the Calderbank-Shor-Steane (CSS) code with the three-stage secure quantum communication protocol. Lastly, it allows for arbitrary qubits to be transmitted from sender to receiver making the proposed protocol general purpose.

Time-delay signature elimination of chaotic laser via a self-feedback antisymmetric resonator.

A scheme for generating a chaotic output from a semiconductor laser while eliminating the time-delay signature (TDS) is proposed, leveraging the multi-path feedback provided by a self-feedback antisymmetric coupling fiber ring resonator (SACFRR). A theoretical model is developed to elucidate the feedback perturbation process in the proposed structure. The multi-path feedback can be modeled by incorporating the unit impulse response of the SACFRR into the modified Lang-Kobayashi-based model. We successfully eliminated the TDS using the SACFRR structure in our experimental demonstration. Further investigation into the impact of the coupling coefficient on the TDS revealed that the optimal value is 0.3, which results in the largest mapping area with the TDS below 0.03. The proposed structure is highly effective and simple to implement and integrate. As a result, the chaotic laser generated by this structure can serve as an efficient optical source for encrypted communications, chaotic Lidar, and random bit generation.

Input panel for handwriting detection based on triboelectric nanogenerator

Handwritten signatures are widely used in our daily lives, which focuses on how to obtain handwritten information comprehensively and effectively. Triboelectric nanogenerators can sensitively sense externally applied trigger signals and can be used to collect user handwriting signals and related features for handwriting input character detection and user identification. In this paper, an intelligent handwriting input panel based on triboelectric nanogenerator is proposed, which, combined with deep learning algorithms, achieves an accuracy of 99.32%, 98.96%, 99.14%, and 99.53% for recognizing handwritten signals in Arabic numerals, English words, Chinese characters, and different users, respectively. It is also possible to use different encoding methods for encrypted communication to transmit signals on the proposed handwriting input panel, demonstrating its potential applications. The results show that the smart handwritten input panel has great potential for personal handwritten signature recognition, privacy information, and human–computer interaction.

Ultra‐Low BER Encrypted Communication Based on Self‐Powered Bipolar Photoresponse Ultraviolet Photodetector

AbstractUnderwater visible light communication often faces risks such as susceptibility to external light source interference, signal distortion, and information leakage. The cuprous oxide modified gallium oxide nanorod arrays detector prepared in this study can generate bipolar photocurrents under dual‐wavelength ultraviolet light without external bias voltage. Based on it, the constructed self‐powered ultraviolet ASCII code communication system not only reduce power consumption but also significantly enhance anti‐interference capability. Furthermore, the developed solar‐blind ultraviolet AMI and HDB3 ternary code communication systems can realize non‐visible light secure communication and automatic error correction. Notably, the encrypted communication system assembled using the additive property of bipolar photocurrents can also significantly improve communication security and reliability, whose bit error rate is only 0.26‰. This achievement contributes to technical advancements in underwater channel encryption and provides valuable insights for the further application of encrypted communication coding techniques.

Scenarios for Optical Encryption Using Quantum Keys

Optical communications providing huge capacity and low latency remain vulnerable to a range of attacks. In consequence, encryption at the optical layer is needed to ensure secure data transmission. In our previous work, we proposed LightPath SECurity (LPSec), a secure cryptographic solution for optical transmission that leverages stream ciphers and Diffie–Hellman (DH) key exchange for high-speed optical encryption. Still, LPSec faces limitations related to key generation and key distribution. To address these limitations, in this paper, we rely on Quantum Random Number Generators (QRNG) and Quantum Key Distribution (QKD) networks. Specifically, we focus on three meaningful scenarios: In Scenario A, the two optical transponders (Tp) involved in the optical transmission are within the security perimeter of the QKD network. In Scenario B, only one Tp is within the QKD network, so keys are retrieved from a QRNG and distributed using LPSec. Finally, Scenario C extends Scenario B by employing Post-Quantum Cryptography (PQC) by implementing a Key Encapsulation Mechanism (KEM) to secure key exchanges. The scenarios are analyzed based on their security, efficiency, and applicability, demonstrating the potential of quantum-enhanced LPSec to provide secure, low-latency encryption for current optical communications. The experimental assessment, conducted on the Madrid Quantum Infrastructure, validates the feasibility of the proposed solutions.

The Court of Justice in Staatsanwaltschaft Berlin v. M.N. (EncroChat): From cross-border, data-driven police investigations to evidence admissibility

The ruling of the Grand Chamber of the Court of Justice of the EU (CJEU) in Case C-670/22 Staatsanwaltschaft Berlin v. M.N. addresses the realities of data-driven police investigations in the light of the Directive 2014/41/EU on the European Investigation Order (EIO Directive). In particular, it examines the cross-border, digital investigation of the encrypted communication network EncroChat and the use and sharing of intercepted telecommunication data in criminal proceedings. In doing so, the CJEU offers clarifications as to the interpretation of the term ‘issuing authority’ under the EIO Directive and the proportionality requirements for issuing an EIO, taking into account its prior jurisprudence on the implications of access to traffic and location data for the private life of the persons concerned. More importantly, this CJEU ruling takes a stand on admissibility of intercepted data as evidence in criminal proceedings, considering the technical complexity of contemporary investigation measures and the secrecy of said police methods. Overall, although this judgment seems to hold no great surprises regarding the interpretation of the EIO Directive's provisions, it underscores the important role of the national legislator and national courts in striking the right balance between rearing the rewards of data-driven policing and safeguarding defence rights.

Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive Survey

The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detection. The Transport Layer Security (TLS) 1.3 protocol, introduced in 2018, has gained rapid popularity due to its enhanced security features and improved performance. However, TLS 1.3’s security enhancements, such as encrypting more of the handshake process, present unprecedented challenges for encrypted traffic analysis, rendering traditional methods designed for TLS 1.2 and earlier versions ineffective and necessitating the development of novel analytical techniques. This comprehensive survey provides a thorough review of the latest advancements in TLS 1.3 traffic analysis. First, we examine the impact of TLS 1.3’s new features, including Encrypted ClientHello (ECH), 0-RTT session resumption, and Perfect Forward Secrecy (PFS), on existing traffic analysis techniques. We then present a systematic overview of state-of-the-art methods for analyzing TLS 1.3 traffic, encompassing middlebox-based interception, searchable encryption, and machine learning-based approaches. For each method, we provide a critical analysis of its advantages, limitations, and applicable scenarios. Furthermore, we compile and review key datasets utilized in machine learning-based TLS 1.3 traffic analysis research. Finally, we discuss the main challenges and potential future research directions for TLS 1.3 traffic analysis. Given that TLS 1.3 is still in the early stages of widespread deployment, research in this field remains nascent. This survey aims to provide researchers and practitioners with a comprehensive reference, facilitating the development of more effective TLS 1.3 traffic analysis techniques that balance network security requirements with user privacy protection.

A European right to end-to-end encryption?

In Podchasov v Russia, the European Court of Human Rights unanimously held that a Russian statutory obligation on ‘internet communications organisers’ to provide information to state authorities that allowed for the decryption of encrypted communications was a disproportionate interference with Article 8 because the available technical means of decryption risked weakening the security of communications for all users of the service. This is significant as authorities in the UK and EU may seek to implement similar statutory obligations on communications service providers.

Quantum pairwise-parallel mismatch attack on Kyber

Abstract Quantum algorithm uses the quantum parallel method to calculate, which can better solve the encryption and decryption problems in cryptography and secure communication. This paper proposes a quantum pairwise-parallel mismatch attack on Kyber using the quantum binary search method. We first give quantum search methods for finding the secret key and show that our method can be applied to Kyber. Then, According to the proposed quantum search method, we compute the number of queries required and computational complexity for recovering the full key on Kyber. Compared with the existing results, our improved attack significantly reduces the number of queries and computational complexity.

Machine Learning-Driven Assessment and Security Enhancement for Electronic Health Record Systems

The digitalized patient-centric system, the Electronic Health Record (EHR), is a platform where comprehensive health information is stored, managed, and accessed electronically. The primary findings of this study aim to secure sensitive patient data and increase overall system resilience by demonstrating that machine learning can evaluate vulnerabilities and improve the security of Electronic Health Record (EHR) systems. This research examines the prospects of incorporating machine learning-driven assessment tools and safety improvements in EHRs to enhance data protection in the healthcare industry. The proposed method utilizes the implementation of machine learning classifiers, specifically the XGBoost and LightGBM models. These classifiers are employed to enhance various aspects of the system, such as data protection and security, within the framework of EHRs. The study emphasizes the efficiency of these machine learning classifiers in ensuring that EHR systems are secure enough to deal with any problem that may occur due to threats posed by external factors or hackers. The findings reveal that the XGBoost model always has outstanding performance, with a near-perfect Receiver Operating Characteristic Curve (ROC) having an AUC equal to 1.00, indicating close to perfect accuracy in distinguishing positive from negative cases. Similarly, LightGBM has a perfect ROC curve as well. Therefore, its performance would be considered flawless. Consequently, future developments could lead to sophisticated machine learning models besides those that have already been developed. Improving data storage through encryption and building safer communication protocols should also be considered to make these systems withstand new security problems. Thus, this study contributes to the existing literature on applying technology to safeguard vulnerable medical records while fostering a safe and efficient healthcare ecosystem.

True Random Number Generator Based on Chaotic Oscillation of a Tunable Double-Well MEMS Resonator.

Chaotic systems have aroused interest across various scientific disciplines such as physics, biology, chemistry, and meteorology. The deterministic but unpredictable nature of a chaotic system is an ideal feature for random number generation. Microelectromechanical systems (MEMS) are a promising technology that effectively harnesses chaos, offering advantages such as a compact footprint, scalability, and low power consumption. This paper presents a true random number generator (TRNG) based on a double-well MEMS resonator integrated with an actuator and position sensor. The potential energy landscape of the proposed MEMS resonator is actively tunable with a direct current voltage. Experimental demonstrations of tunable bistability and chaotic resonance are reported in this paper. A chaotic time sequence is generated through piezoresistive sensing of the position of the MEMS resonator once it is driven into the chaotic regime. Subsequently, the randomness of the bit sequence, achieved by applying the exclusive or function to a digital chaotic sequence and its delayed differential is confirmed to meet the National Institute of Standards and Technology specifications. Moreover, the throughput and energy efficiency of the proposed MEMS-based TRNG can be adjusted from 50 kb s-1 and 0.44 pJ per bit at a low energy barrier to 167 kb s-1 and 6.74 pJ per bit at a high energy barrier by changing the MEMS device's potential well. The tunability of the proposed double-well MEMS resonator not only offers continuous adjustments in the energy efficiency of TNRG but also unveils vast and diverse research opportunities in analog computing, encryption, and secure communications.