Key Encryption Research Articles

Observers for hyperbolic systems with multiple delays in the nonlocal boundary condition and its application to secure image encryption

In this article, we propose a step-by-step observer design for a first-order linear hyperbolic system with multiple delays in the nonlocal boundary condition. In the proposed approach inspired by the backstepping methodology, the observer gains are obtained following seven successive steps. Next, we present the application of this observer design in the context of image encryption. We consider a system where a local nonlinear term causing a chaotic vibration is added to the nonlocal boundary condition with time lags. A synchronization system for this chaotic system can be constructed using the same observer design method. The chaotic states generated by both systems can be used to encrypt and decrypt image data. The time lags, and weight functions included in the nonlocal boundary condition play the role of common encryption keys. By considering a system that includes arbitrarily many delays, this approach allows for a significant increase in the number of common encryption keys compared to conventional methods, resulting in greatly improved communication security.

Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation

Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods.

Convergent encryption enabled secure data deduplication algorithm for cloud environment

SummaryThe exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security.

Context-Committing Security of Leveled Leakage-Resilient AEAD

During recent years, research on authenticated encryption has been thriving through two highly active and practically motivated research directions: provable leakage resilience and key- or context-commitment security. However, the intersection of both fields had been overlooked until very recently. In ToSC 1/2024, Struck and Weishäupl studied generic compositions of encryption schemes and message authentication codes for building committing leakage-resilient schemes. They showed that, in general, Encrypt-then-MAC (EtM) and MAC-then-Encrypt (MtE) are not committing while Encrypt-and-MAC (EaM) is, under plausible and weak assumptions on the components. However, real-world schemes are rarely strict blackbox constructions. Instead, while various leakage-resilient schemes follow blueprints inspired by generic compositions, they often tweak them for security or efficiency.In this paper, we study two blueprints, the first one based on EtM for one of the strongest possible levels of leakage resilience. The second one is a single-pass framework based on leveled implementations. We show that, with a careful selection of the underlying primitives such as with identical encryption and authentication keys and a collision-resistant PRF as the MAC, these blueprints are committing. Our results do not contradict the results by Struck and Weishäupl since we pose more, but practically-motivated, requirements on the components. We demonstrate the practical relevance of our results by showing that our results on those blueprints allow us to easily derive proofs that several state-of-the-art leakage-resilient schemes are indeed committing, including TEDT and its descendants TEDT2 and Romulus-T, as well as the single-pass scheme Triplex.

Cap like trajectories in 5D chaotic tangent hyperbolic memristive model: fractional calculus and encryption

This research aims to investigate the influence of model parameters and fractional order on a novel mathematical model with tangent hyperbolic memristor. This investigation conducted by applying Lyapunov exponents and bifurcation analysis. We utilize the Lyapunov exponent theory to understand and characterize these chaotic behaviors under fractional indices. The Lyapunov exponent, bifurcation, and phase diagrams have been depicted to explore the intricate dynamics of the chaotic system governed by the chaotic equation. A novel approach termed Atangana-Baleanu-Caputo (ABC) fractional derivative (FD) to generate phase portraits and gain insights into the system’s behavior. The random numbers generated by the chaotic system are employed to distort the image through an amalgamated image encryption (AIE) algorithm. Subsequently, the integrity of the scrambled image has been assessed using various image security evaluation methods to reinforce the notion that combining the chaotic system and image can constitute a valuable encryption key. Finally, the chaotic model circuit realization uses active and passive components, and the outcomes are compared with the numerical simulations.

Continuous Integration for New Service Deployment and Service Validation Script for Vault

Modern cloud-native applications demand robust security measures to safeguard sensitive data such as passwords, API keys, and encryption keys. Managing these secrets securely within Kubernetes clusters presents a significant challenge. In response, this project proposes a comprehensive solution leverag- ing HashiCorp Vault, Kubernetes, and Docker to enhance secret management and strengthen overall security posture.HashiCorp Vault serves as a centralized secrets management tool, provid- ing encryption, access control, and auditing functionalities. By integrating Vault with Kubernetes, secrets can be dynamically generated, securely stored, and automatically injected into ap- plication pods at runtime. This approach reduces the exposure of sensitive information within containerized environments and mitigates the risk of unauthorized access. The project architecture involves deploying Vault within the Kubernetes cluster, utilizing Docker containers for seamless encapsulation and portability. Kubernetes’ native integrations with Vault, such as the Kubernetes Auth method and the Vault Agent Injector, streamline the authentication and authorization processes, ensuring secure communication between applications and Vault. The project involves deploying Vault in Kubernetes for secrets management, ensuring High Availability. It focuses on generating, storing, and managing secrets securely, leveraging Vault’s dynamic secrets engine for automatic rotation. Integration with Kubernetes employs authentication methods like Service Accounts and RBAC for granular access control. Dockerization ensures application consistency and portability, with Vault Agent containers enabling seamless secret injection. Security best practices, including least privilege access and encryption, are prioritized, along with regular auditing and monitoring. Overall, the project aims to establish a robust secrets management solution within Kubernetes while empha- sizing resilience, security, and compliance in handling sensitive information. Index Terms—Docker, DevOps, CI/CD, Automation, Secrets, Kubernetes, Vault, Security

Temper wolf hunt optimization enabled GAN for robust image encryption

In today’s digital era, the security of sensitive data, particularly in the realm of multimedia, is of paramount importance. Image encryption serves as a vital shield against unauthorized access and ensures the confidentiality and integrity of visual information. As such, the continuous pursuit of robust and efficient encryption techniques remains a pressing concern. This research introduces a Temper Wolf Hunt Optimization enabled Generative Adversarial Network Encryption model (TWHO-GAN), designed to address the challenges of image encryption in the modern digital landscape. TWHO, inspired by the collective hunting behavior of wolf and coyote packs, is employed to generate highly secure encryption keys. This algorithm excels in exploring complex solution spaces, creating robust, attack-resistant keys. In TWHO-GAN model, GANs are employed to create encrypted images that are virtually indistinguishable from their original counterparts, adding a layer of security by generating complex encryption keys and ensuring robust protection against attacks. The GAN component reconstructs the encrypted images to their original form when decrypted with the correct keys, ensuring data integrity while maintaining confidentiality. Further, the significance of the proposed model relies on the TWHO algorithm formulated by the integration of the adaptability and coordinated hunting strategies to optimize the chaotic map generation in image encryption protecting the sensitive visual information from unauthorized access as well as potential threats. Through extensive experimentation and comparative analysis, TWHO-GAN demonstrates superior performance in image encryption, surpassing former methods in terms of Cs, 𝐻𝑖𝑠C, MSE, PSNR, RMSE, and SSIM attaining values of 0.93, 94.19, 3.274, 59.70 dB, 1.8095, and 0.940 respectively for 5 numbers of images. Moreover, the TWHO-GAN approach attained the values of 0.91,92.22, 2.03, 49.74 dB, 1.42, and 0.88 for Cs, HisC, MSE, PSNR, RMSE, and SSIM respectively utilizing the Airplanes dataset. The model exhibits robust resistance to various attacks, making it a compelling choice for secure image transmission and storage.

Securing Decentralized Storage in Blockchain: A Hybrid Cryptographic Framework

Abstract The evolution of decentralized storage, propelled by blockchain advancements, has revolutionized data management. This paper focuses on content security in the InterPlanetary File System (IPFS), a leading decentralized storage network lacking inherent content encryption. To address this vulnerability, we propose a novel hybrid cryptographic algorithm, merging AES 128-bit encryption with Elliptic Curve Cryptography (ECC) key generation. The algorithm includes ECC key pairs, random IV generation, and content/AES key encryption using ECC public keys. Benchmarking against standard AES 256-bit methods shows a significant 20% acceleration in encryption speed and a 16% increase in decryption efficiency, affirming practicality for enhancing IPFS content security. This research contributes to securing decentralized storage and provides a performance-driven solution. The promising results highlight the viability of the proposed approach, advancing understanding and mitigating security concerns in IPFS and similar systems.

Encrypted image processing using compression and reversible data hiding

Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available.

Data Security and Storage Administration with Dynamic Encryption Key Management

Data Security and Storage Administration with Dynamic Encryption Key Management

A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm

AbstractSharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key is compromised, posing a security threat. A secured cloud‐based medical data‐sharing system is proposed using a hybrid encryption model called A‐BRSA, which combines attribute‐based encryption (ABE) and B‐RSA encryption. The system utilises the Salp‐Ant Lion Optimisation Algorithm for optimal key selection. The encrypted data is stored in the cloud and transmitted to the recipient, where it is decrypted using A‐BRSA‐based decryption. The study measures turnaround time, encryption time, decryption time, and restoration efficiency to evaluate the system's performance. The results demonstrate the effectiveness of the A‐BRSA model in ensuring secure medical data sharing in cloud‐based s‐healthcare systems.

Multi-key and Multi-input Predicate Encryption (for Conjunctions) from Learning with Errors

We put forward two natural generalizations of predicate encryption (PE), dubbed multi-key and multi-input PE. More in details, our contributions are threefold.Definitions. We formalize security of multi-key PE and multi-input PE following the standard indistinguishability paradigm, and modeling security both against malicious senders (i.e., corruption of encryption keys) and malicious receivers (i.e., collusions).Constructions. We construct adaptively secure multi-key and multi-input PE supporting the conjunction of poly-many arbitrary single-input predicates, assuming the sub-exponential hardness of the learning with errors (LWE) problem.Applications. We show that multi-key and multi-input PE for expressive enough predicates suffices for interesting cryptographic applications, including non-interactive multi-party computation (NI-MPC) and matchmaking encryption (ME). In particular, plugging in our constructions of multi-key and multi-input PE, under the sub-exponential LWE assumption, we obtain the first ME supporting arbitrary policies with unbounded collusions, as well as robust (resp. non-robust) NI-MPC for so-called all-or-nothing functions satisfying a non-trivial notion of reusability and supporting a constant (resp. polynomial) number of parties. Prior to our work, both of these applications required much heavier tools such as indistinguishability obfuscation or compact functional encryption.

Enhancing the Secure Transmission of Data Over Optical Fiber Networks from Source to Destination

ABSTRACT The quantum key distribution (QKD) method allows for the safe creation of encryption keys between trusted entities for secure communication. The suggested system is designed to enable secure communication between multiple parties (a single sender, Alice, and multiple receivers, Bobs) connected via a public channel susceptible to interception. It enables Alice to privately share encryption keys with each Bob over dedicated quantum channels, which can then be used to decrypt messages sent over the public network. The procedure involves Alice generating optical signals with randomly assigned properties such as intensity, phase, polarization, and frequencies. Alice randomly sends either signal states carrying information or decoy states with different frequencies to the receivers (Bobs) over the quantum channels. She also sends a synchronization signal for timing alignment with the quantum signals over the optical links. At the receiving end, the synchronization pulse timestamps the incoming quantum signals. Each Bob then randomly alters the frequencies of the received quantum signals and randomly measures their phase or polarization. The key is created from the frequency, phase, or polarization information of the quantum signals, resulting in longer keys. Key management agents manage the securely QKD-generated keys to encrypt information before sending it to the intended receivers. Essentially, this framework establishes authenticated, private communication by using QKD over dedicated ultra-secure quantum links to distribute decryption keys, ensuring that only intended users can access the content. The approach is designed to ensure end-to-end secure communication across the network.

Improved Vigenere Cipher-RSA-Based Medical Image Security Through Multiple Encryption Keys

Improved Vigenere Cipher-RSA-Based Medical Image Security Through Multiple Encryption Keys

Image cryptosystem with image key using integral disproportion

The subject matter of this article is the process of securing visual data through encryption. The goal of this study is to develop a new approach for image encryption and decryption using an image as a key, where security and performance are comparable or superior to alternatives. The tasks to be solved are as follows: developing a symmetric image cryptosystem for both greyscale and colored images in any format that can use an arbitrary image as a security key; adapting the capabilities of integral disproportionality for image encryption. The encryption method applies an integral disproportion formula for each pair of pixels’ components for both the input image and the key image starting from the first one. The output is a ciphered object represented as an array of real numbers, with decryption being the inverse process. The result is a novel approach to image encryption, proposing a new cryptosystem in which an arbitrary image of varying size, format, or content serves as the encryption key. The proposed symmetric full-encryption cryptosystem is applicable to both greyscale and colored images in any format. The method employs a frequency-based approach that influences the values of pixels rather than their positions. In contrast to mainstream approaches in this area, the representation of a cipher is not an image but a binary array. The proposed algorithm is simple to implement. The proposed method appears to meet the required requirements in both security and performance. Conclusions. The scientific novelty of the results obtained is as follows: This research introduces a new image encryption method, which is unique in its application of integral disproportionality to alter pixel values, differing from traditional encryption methods. This provides a practical yet secure option to the existing encryption techniques.

Quantum encryption of healthcare images: Enhancing security and confidentiality in e‐health systems

AbstractEnsuring the security and privacy of patient data in e‐healthcare systems that rely on cloud computation is of utmost importance. Traditional encryption is no longer resistant to quantum attacks and safeguards sensitive medical images. To tackle this issue, robust security countermeasures are proposed by integrating quantum encryption with a cloud‐based healthcare system. The encryption scheme utilizes the Generalized Novel Enhancement Quantum Representation (GNEQR) and the Novel Enhancement Quantum Representation (NEQR) to provide a framework for representing color and grayscale healthcare images. The proposed quantum algorithm uses quantum logic for image scrambling, which is combined with the encryption key by the Xor quantum gate. The encryption key is generated by 9D chaotic and permutated before encryption. Finally, channel re‐ordering is applied for color images. The simulation results for 15 medical tests with an encryption key space >2600 on a developed e‐healthcare system demonstrate the effectiveness and reliability of the proposed work where the average number of pixels change rates was 99.82, while the unified average change intensity rate was 33.51, entropy was 7.9, the horizontal, vertical, and diagonal correlation coefficients averaged 0.000533333, 0.000706667, and 0.00076, respectively. Finally, the mean squared error (MSE) between the original and encrypted images was 10203.72. These findings improve digital healthcare by revealing the solutions' performance, security, and efficacy.

Secure Data in the Cloud with a Robust Hybrid Cryptographic Approach

Cloud security combines hybrid symmetric encryption and key management to protect user privacy. Our approach can be easily scaled to meet growing cloud data security needs. Cloud computing's basic tenet is resource sharing, which is used for data storage, analysis, and management. Since many people use cloud services and are spread out across the internet, they present many security risks. Hybrid clouds, which can be accessed from anywhere, provide numerous advantages. The security solution (infrastructure as a service) is compatible with many PaaS, SaaS, and even IaaS cloud services. Furthermore, it is consistent with the vast majority of existing cloud solutions. Cloud computing security can be strengthened by using hybrid public key cryptosystems. Concerns about data security have made many businesses wary of cloud computing. The study aims to secure cloud computing by combining the Rabin and Rivest-Shamir-Adleman (RSA) cryptographic models. Analyzing how well a combination of methods generates secure encryption and decryption keys for data. The pure RSA system has longer response times and requires fewer computations compared to the hybrid system.

Toward FPGA Intellectual Property (IP) Encryption from Netlist to Bitstream

Current IP encryption methods offered by FPGA vendors use an approach where the IP is decrypted during the CAD flow, and remains unencrypted in the bitstream. Given the ease of accessing modern bitstream-to-netlist tools, encrypted IP is vulnerable to inspection and theft from the IP user. While the entire bitstream can be encrypted, this is done by the user, and is not a mechanism to protect confidentiality of 3rd party IP. In this work we present a design methodology, along with a proof-of-concept tool, that demonstrates how IP can remain partially encrypted through the CAD flow and into the bitstream. We show how this approach can support multiple encryption keys from different vendors, and can be deployed using existing CAD tools and FPGA families. Our results document the benefits and costs of using such an approach to provide much greater protection for 3rd party IP.

Securing Social Media Imagery: GAN-Driven Encryption and CNN Analysis with DEA Protection

Generative adversarial networks (GANs) transform low-dimensional random noise into a photorealistic picture. The use of such misleading pictures laden with irrelevant information on social media platforms might lead to serious and challenging issues. The goal of this study is to create and identify the affected GAN pictures. Additionally, enhanced GAN detection accuracy by preprocessing and segmentation feature extraction. This study examines the effectiveness of several learning-based methods for detecting image-to-image translation. Generative adversarial networks use deep learning approaches like convolutional neural networks for generative modelling and the DEA (Data encryption standard) is used to produce the encryption key. To accurately identify fake images, an effective picture forgery detector is necessary. Recent advancements in generative adversarial networks (GANs) have been focused on producing photorealistic pictures quickly and successfully. However, GANs can complicate visual forensics and model attribution. Data from one source and pictures from another can have a wide range of uses, including in fields like computer vision, video, and language processing. The investigation of some photos reveals that both conventional and deep learning detectors may reach up to 95% detection accuracy. However, only deep learning maintains good accuracy on compressed data. An article that explains how to spot GAN-generated false images on social media also provides context on GANs and the theoretical concepts underlying them.

Unbounded non-zero inner product encryption

In a non-zero inner product encryption (NIPE) scheme, ciphertexts and keys are associated with vectors from an inner-product space. Decryption of a ciphertext for x→ is allowed by a key for y→ if and only if the inner product 〈x→,y→〉≠0. Existing constructions of NIPE assume the length of the vectors are fixed apriori. We present the first constructions of unbounded non-zero inner product encryption (UNIPE) with constant sized keys. Unbounded here refers to the size of vectors not being pre-fixed during setup. Both constructions, based on bilinear maps, are proven selectively secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.Our constructions are obtained by transforming the unbounded inner product functional encryption (IPFE) schemes of Dufour-Sans and Pointcheval (ACNS 2019), one in the strict domain setting and the other in the permissive domain setting. Interestingly, in the latter case, we prove security from DBDH, a static assumption while the original IPFE scheme relied on an interactive parameterised assumption. In terms of efficiency, features of the IPFE constructions are retained after transformation to NIPE. Notably, the public key and decryption keys have constant size.