Encryption Research Articles

MODELING OF OPTIMAL MULTI KEY HOMOMORPHIC ENCRYPTION WITH DEEP LEARNING BIOMETRIC BASED AUTHENTICATION SYSTEM FOR CLOUD COMPUTING

More recently, cloud computing (CC) has gained considerable attention among research communities and business people. Inspite of the advantages of CC, security, and privacy remains a challenging problem. Therefore, biometric authentication systems have been employed and fingerprint is considered as widely employed to attain security. In addition, image encryption techniques can be used to encrypt the fingerprint biometric image to add an extra level of security. Based on these motivation, this study designs an optimal multikey homomorphic encryption (OMHE) with stacked autoencoder (SAE) based biometric authentication system for CC environment. The proposed OMHE-SAE model aims to encrypt the biometrics using OMHE technique and then verification takes place using SAE model. In addition, the OMHE technique involves the optimal key generation process using sandpiper optimization (SPO) algorithm to effectively choose the keys for encryption and decryption. Furthermore, the verification of decrypted biometrics takes place by the use of SAE model. A wide range of simulation analyses take place on benchmark datasets and the experimental outcomes portrayed the betterment of the OMHE-SAE More than cutting edge technology.

Technique for Searching Data in a Cryptographically Protected SQL Database

The growing popularity of data outsourcing to third-party cloud servers has a downside, related to the serious concerns of data owners about their security due to possible leakage. The desire to reduce the risk of loss of data confidentiality has become a motivating start to developing mechanisms that provide the ability to effectively use encryption to protect data. However, the use of traditional encryption methods faces a problem. Namely, traditional encryption, by making it impossible for insiders and outsiders to access data without knowing the keys, excludes the possibility of searching. This paper presents a solution that provides a strong level of confidentiality when searching, inserting, modifying, and deleting the required sensitive data in a remote database whose data are encrypted. The proposed SQL query processing technique allows the DBMS server to perform search functions over encrypted data in the same way as in an unencrypted database. This is achieved through the organization of automatic decryption by specially developed secure software of the corresponding data required for search, without the possibility of viewing these data itself. At that, we guarantee the integrity of the stored procedures used and special tables that store encrypted modules of special software and decryption keys, the relevance and completeness of the results returned to the application. The results of the analysis of the feasibility and effectiveness of the proposed solution show that the proper privacy of the stored data can be achieved at a reasonable overhead.

Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption

Background and Objective: The Internet of medical things is enhancing smart healthcare services using physical wearable sensor-based devices connected to the Internet. Machine learning techniques play an important role in the core of these services for remotely consulting patients thanks to the pattern recognition from on-device data, which is transferred to the central servers from local devices. However, transferring personally identifiable information data to servers could become a source for hackers to steal from, manipulate and perform illegal activities. Federated learning is a new branch of machine learning that creates directly training models from on-device data and aggregates these learned models on the servers without centralized data. Another way to protect data confidentiality on computer systems is data encryption. Data encryption transforms data into another form that only users with authority to a decryption key can read. In this work, we propose a novel method enabling preservation of client privacy and protection of client biomedical data from illegal hackers while transmitting through the Internet.Methods: We propose a method applying 3-dimensional convolutional neural networks for human activity recognition using multiple sensory data. In order to protect the data, we apply the bitwise XOR operator encryption technique. Then, we extend our 3-dimensional convolutional neural network methods to both traditional federated learning and the federated learning based on multi-key homomorphic encryption using the proposed encrypting data.Results: Based on leave-one-out-cross-validation, the 3-dimensional method obtains an accuracy of 94.6% and of 94.9% (without data encrypting and without federated learning) tested on two different benchmarked datasets, Sport and DaLiAC respectively. Accuracy is decreased slightly to 89.5% (from 94.6% of the baseline) when we use the proposed encrypting data method. However, the encryption-data-based method still has a potential result compared to the state-of-the-art which only uses raw data. In addition, the proposed full federated learning scheme of this work shows that illegal persons who somehow can get the trained model transmitted via networks cannot infer the private result.Conclusions: This novel method for sensory data representation which translates temporal and frequency bio-signal values to voxel intensities that can encode 3-dimensional activity images. Secondly, the proposed 3-dimensional convolutional neural network methods outperform other deep-learning-based human activity recognition approaches. Finally, extensive experiments show the proposed data-encrypted federated learning approach can achieve feasibility in terms of efficiency in privacy preservation.

Refinement of Voting System through Visual Cryptography and Multi-factor Authentication to Further Mitigate Clone Phishing Attack

Background: The growing concern over phishing attacks on voting systems, particularly with the rise of online voting, has highlighted vulnerabilities in elections. The convenience of internet voting has led to security risks like clone phishing attacks. While online voting offers accessibility benefits, security, privacy, and usability issues have arisen. Multi-factor authentication (MFA) has been proposed to enhance security in mobile internet voting systems. Visual cryptography, dividing images into shares for decentralized data storage, is suggested to counter clone phishing. MFA's effectiveness in various sectors is established, and secure voting systems combining visual cryptography and blockchain have been proposed. This research sought to bolster the security of the voting system using visual cryptography and multi-factor authentication (MFA), with the goal of increasing voter trust and the reliability of the voting procedure, by designing a secure system and evaluating its performance, accuracy, and accessibility.
 Methodology: The researchers developed an improved voting system using visual cryptography and multi-factor authentication, assessed its performance against Eligo and Voxvote, utilized Java for programming, MYSQL via XAMPP for the database, HTML, CSS, JavaScript, and PHP (Laravel) for client-server sides. The Scrum agile methodology was followed, employing brief sprints for adaptive development. Evaluation was done through web tools and benchmarks. The system's architecture and flowchart were presented, featuring an interactive GUI, Java 2 platform, MySQL, PHP 7, and specific hardware/software requirements. System setup included database and server configuration, fingerprint scanner installation, and Java runtime setup. Deployment involved executing a built Java program, and system testing was conducted on Windows 10, utilizing the Apache server and initiating the "Electronic Voting" program for online multi-factor authentication.
 Results: Achieving a performance rate of 92%, the developed system surpassed its competitors Eligo and Voxvote, which achieved scores of 15% and 33% correspondingly, as indicated by the data. Eligo and Voxvote attained scores of 88% and 80% individually, whereas the newly designed system obtained a rating of 93% in terms of accessibility. Nonetheless, the research also highlighted specific downsides, encompassing intricacy, reluctance to embrace change, and technological barriers. To tackle these issues and enhance the adoption of such systems, these limitations underscore the necessity for further investigation and advancement.
 Conclusion: The study demonstrates that integrating multi-factor authentication and visual cryptography significantly enhances voting system security, reducing clone phishing risks. Visual cryptography secures decryption keys, preserving voting integrity, while multi-factor authentication adds defence against unauthorized access. The researcher's online voting system outperforms competitors in performance metrics and accessibility. The study underscores the importance of combining these techniques for improved voting system reliability and security.

AC-SDVN: An Access Control Protocol for Video Multicast in Software Defined Vehicular Networks

The way to use limited bandwidth resources to achieve high-quality video services in vehicular networks is an important research topic. A large number of studies have shown that the fast-growing field of software defined networking (SDN) can provide solutions to the problems encountered by traditional IP networks when implementing video multicasting. However, there is no research addressing the security issues for this scenario. In this paper, we explore the application scenarios of video multicast in software defined vehicular networks (SDVN), and propose a secure and effective access control protocol to solve multicast security issues. This protocol realizes the authentication of multicast video requesting vehicles and RSUs. According to the authentication results, the SDN controller constructs multicast paths that only reach legitimate RSUs and vehicles, and only the vehicle passing the authentication can obtain video decryption keys. The protocol resists common attacks and satisfies the security requirements in vehicular networks. In addition, the scheme supports batch verification, which reduces the time cost of authentication, and adopts broadcast encryption technology to effectively reduce the communication load. Compared with related schemes, our protocol performs better in terms of computation and communication cost, packet loss rate, and time delay.

Optical 3D information encryption and rapid decryption via interferenceless coded aperture correlation holography

Holographic encryption has obvious advantages in large key space and anti-attack ability, but the corresponding coherent noise and optical alignment requirement greatly affect the encryption efficiency and decryption quality. Here, we present an optical 3D information encryption method based on the interferenceless coded aperture correlation holography (I-COACH), which consists of a coded phase mask and a lens. The speckle pattern, which is the convolution of the point spread hologram (PSH) with the object intensity distribution recorded in the image sensor plane, is used as the ciphertext, and these pre-recorded PSH are utilized as the decryption key. Due to the spatial decorrelation characteristics of the PSH at different axial positions, the plaintext can be decrypted only when the ciphertext is cross-correlated with the PSH of its corresponding axial position. Therefore, a 3D object composed of multiple 2D planes can only be decrypted by the PSH of its corresponding depth. For rapid and efficient decryption, we introduce the space division multiplexing technology. By multiplexing coded phase masks at different positions, the PSH at two positions can be simultaneously recorded in single-shot. In addition to improving the encryption efficiency and decryption quality, both numerical simulation and experimental results demonstrate the excellent security and robustness of the proposed I-COACH based 3D information encryption method.

Encryption and Tokenization of Card Security using Cryptographic Algorithms

There are several alternative strategies that can be used when utilising cryptography to encrypt card information. Encryption's main objective in this situation is to prevent sensitive card information from being intercepted and used fraudulently, including the card number, expiration date, and security code. Using SSL (Secure Socket Layer) or TLS (Transport Layer Security), which establishes a safe, encrypted connection between a website or app and the server where the card information is stored, is one popular method of encrypting card information. This is frequently used for online transactions, such as when paying with a mobile app or buying something from an e-commerce website. Using end-to-end encryption is an alternative strategy that guarantees that card information is encrypted from the time it is entered into a device, website, or app until it reaches its destination. This is frequently used with tokenization, which exchanges the sensitive card information for a singular token that can be safely kept and used for upcoming transactions without disclosing the actual card data. Additionally, a number of cryptographic protocols and algorithms, including AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and ECC (Elliptic Curve Cryptography), can be used to encrypt card information. These algorithms and protocols jumble the data using intricate mathematical computations so that it cannot be decoded without the right decryption key.

Image transformation based on optical reservoir computing for image security

Visually secure image encryption method can provide an added dimension of protection for image compared to conventional image encryption scheme that encrypts a plain image to a noise-like cipher image. Here we introduce a novel perspective based on image transformation to perform visually secure image encryption by constructing an optical reservoir computing (ORC) system. In this paper, we first design a novel optical dynamics model to construct a dual-channel signal injection ORC system, and to provide chaotic keys when the optical dynamics model operating at chaotic states. Then, the established ORC system is used to reveal the underlying transformation relationship between a plain image and a visually meaningful image both with the same size. One important role of such relationship is to provide evidence for the determination of cipher image that allows visually meaningful image to be directly set as cipher image, prompting a high visual security of that, and another role is to facilitate the calculation of necessary decryption keys in order to obtain a high-quality decrypted image. Compared with other existing encryption methods, this work overcomes a challenge of the inability in simultaneously achieving low burden cost, high visual security of cipher image, and high quality of decrypted image, and the simulation results and performance analysis demonstrate that the proposed scheme is effective and secure. Moreover, this study also offers an inspirational implication to develop novel alternative framework with respect to the study of visually secure image encryption and will help other researchers to develop new image security techniques based on transformation methods.

Deception-based Method for Ransomware Detection

Ransomware is a rapidly growing malware threat that encrypts a user's files and demands a ransom for the decryption key. It has caused significant financial harm worldwide and is difficult to detect, especially when it's a new, unknown zero-day ransomware. Most commercial antivirus software relies on signature-based detection, which can be slow and inadequate for swiftly identifying suspicious programs. To tackle these challenges, this paper presents a ransomware protection method utilizing decoy files. Our deception-based protection method enhances ransomware detection with a fair decoy deployment strategy. Our method offers the advantage of robustly detecting ransomware compared to existing deception-based methods. Furthermore, it can effectively address ransomware that employs random access attacks, thereby bypassing deception-based detection techniques. In the evaluation, we provide a comprehensive analysis of our experimental results to vividly demonstrate the efficacy of our proposed method. Specifically, we introduce a random-access attack scenario that could potentially circumvent deception-based protection mechanisms. Furthermore, we assess the resilience of our method against such random-access attacks.

Configurable Encryption and Decryption Architectures for CKKS-Based Homomorphic Encryption.

With the increasing number of edge devices connecting to the cloud for storage and analysis, concerns about security and data privacy have become more prominent. Homomorphic encryption (HE) provides a promising solution by not only preserving data privacy but also enabling meaningful computations on encrypted data; while considerable efforts have been devoted to accelerating expensive homomorphic evaluation in the cloud, little attention has been paid to optimizing encryption and decryption (ENC-DEC) operations on the edge. In this paper, we propose efficient hardware architectures for CKKS-based ENC-DEC accelerators to facilitate computations on the client side. The proposed architectures are configurable to support a wide range of polynomial sizes with multiplicative depths (up to 30 levels) at a 128-bit security guarantee. We evaluate the hardware designs on the Xilinx XCU250 FPGA platform and achieve an average encryption time 23.7× faster than that of the well-known SEAL HE library. By reducing time complexity and improving the hardware utilization of cryptographic algorithms, our configurable CKKS-supported ENC-DEC hardware designs have the potential to greatly accelerate cryptographic processes on the client side in the post-quantum era.

Privacy-Preserving Fine-Grained Redaction with Policy Fuzzy Matching in Blockchain-Based Mobile Crowdsensing

The redactable blockchain has emerged as a promising technique in mobile crowdsensing, allowing users to break immutability in a controlled manner selectively. Unfortunately, current fine-grained redactable blockchains suffer two significant limitations in terms of security and functionality, which severely impede their application in mobile crowdsensing. For security, the transparency of the blockchain allows anyone to access both the data and policy, which consequently results in a breach of user privacy. Regarding functionality, current solutions cannot support error tolerance during policy matching, thereby limiting their applicability in various situations, such as fingerprint-based and face-based identification scenarios. This paper presents a privacy-preserving fine-grained redactable blockchain with policy fuzzy matching, named PRBFM. PRBFM supports fuzzy policy matching and partitions users’ privileges without compromising user privacy. The idea of PRBFM is to leverage threshold linear secret sharing based on the Lagrange interpolation theorem to distribute the decryption keys and chameleon hash trapdoors. Additionally, we have incorporated a privacy-preserving policy matching delegation mechanism into PRBFM to minimize user overhead. Our security analysis demonstrates that PRBFM can defend against the chosen-ciphertext attack. Moreover, experiments conducted on the FISCO blockchain platform show that PRBFM is at least 7.8 times faster than existing state-of-the-art solutions.

Semiassisted Signal Authentication for Galileo: Proof of Concept and Results

A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semi-assisted</i> GNSS authentication concept is proposed, with two main advantages. Firstly, it can be implemented in a GNSS without modifications to the signal plan, provided that the GNSS can transmit an encrypted signal and an open signal with unpredictable and verifiable bits regularly. Secondly, it can work in a receiver for up to several days without any assistance or the possession of a private key, at the expense of some authentication latency. In this concept, parts of the encrypted signal to be transmitted in the future are re-encrypted, published, and downloaded in the receiver. Some seconds after transmission, the satellite openly broadcasts the decryption key, and the receiver performs the a-posteriori correlation with the encrypted signal. The concept is particularized for the Galileo system as Galileo ACAS (Assisted Commercial Authentication Service), based on OSNMA in E1B for the decryption keys and E6C for the encrypted signal. An end-to-end procedure for measurement authentication is proposed and tested, showing its viability.

Event-based fixed-time synchronization of neural networks under DoS attack and its applications

In this paper, the fixed-time synchronization control for neural networks with discontinuous data communication is investigated. Due to the transmission blocking caused by DoS attack, it is intractable to establish a monotonically decreasing Lyapunov function like the conventional analysis of fixed-time stability. Therefore, by virtue of recursive and reduction to absurdity approaches, novel fixed-time stability criteria where the estimated upper bound of settling-time is inherently different from existing results are presented. Then, based on the developed conditions, an event-triggered control scheme that can avoid Zeno behavior is designed to achieve synchronization of master–slave neural networks under DoS attack within a prescribed time. For comparison, the established control scheme is further discussed under the case without DoS attack, and the circumstance that there is no attack or event-triggered mechanism, respectively. Simulation results are finally provided to illustrate the significant and validity of our theoretical research. Especially, in terms of encryption and decryption keys generated from the synchronization behavior of chaotic networks, we specifically discuss the application of the proposed fixed-time synchronization scheme to image and audio encryption.

Achieving Revocable Attribute Group-Based Encryption for Mobile Cloud Data: A Multi-Proxy Assisted Approach

Although proxy-assisted revocable attribute-based encryption provides fine-grained privacy protection and reduces the decryption cost of data users, it inherits several disadvantages in outsourcing decryption. The decryption capability in outsourcing decryption is divided into two parts: the outsourcing transformation key and the user decryption key. The proxy server utilizes transformation keys to transform the ciphertext which can only be decrypted by the users who generated the transformation key. When multiple users with the same attributes request outsourcing transformation, the proxy server must do numerous transformation operations, increasing the computing overhead of the proxy server and the user request response time. To address the aforementioned issues, we propose a multi-proxy assisted revocable attribute group-based encryption (MP-RAGBE) scheme. We form a user group out of users who have the same attributes and send the transformation keys directly to the proxy server. Users in the same group can decrypt the transformed ciphertext, and only a small number of transformation keys need to be updated when revocation occurs. The security and experimental analyses show that the proposed scheme meets the defined security requirements while significantly reducing user request response time, the overhead of transformation key generation, transmission, and ciphertext transformation.

AN ENHANCED SOFTWARE AS A SERVICE (SAAS) ARCHITECTURAL MODEL FOR CLOUD BASED SECURITY USING HYBRID SYMMETRIC ALGORITHM

Protecting cloud data from security breaches, preventing and ensuring safe cloud data is concealed from unauthorized access or infiltration by unwanted users are all part of cloud infrastructure security. This proposed technique is based on ensuring data confidentiality by adding extra-layer of security from the client side, developing a framework that implements the AES 256-bit key and Fernet 128-bits algorithms, which encrypt and decrypt files via random algorithm selection during uploads and downloads. When a file is uploaded to the cloud, the decryption key is recorded in the local server's file log. The software use randomized algorithm selection to provide security to ensure that an attacker having a prior link cannot reset the user account since the attacker cannot guess the token. Furthermore, invalidating this token ensures that the link cannot be used more than once if it was previously logged anywhere. To propose and verify the efficiency of the model the results generated(in quantitative values) are frequency (monobit) testing, with a minimum bit size of 64, given the P-values derived from the above study (i.e., 0.8838, 0.6187, 0.3768, 0.2817, 0.5843, 0.4167). The findings reveal throughput for encryption of 0.91531 and for decryption of 0.4854, space complexity of 0.4854, and entropy of 7.942616667.This research work studies the design of a robust, adaptable, and non-deterministic SaaS application that can be secure for enterprises and subsequent research studies will be conducted in the development.

Multi-key optical encryption based on two-channel incoherent scattering imaging.

Optical encryption has been extensively researched in the field of information security due to its characteristics of being parallel and multi-dimensionsal. However, most of the proposed multiple-image encryption systems suffer from a cross-talk problem. Here, we propose a multi-key optical encryption method based on a two-channel incoherent scattering imaging. In the encryption process, plaintexts are coded by the random phase mask (RPM) in each channel and then coupled by an incoherent superposition to form the output ciphertexts. In the decryption process, the plaintexts, keys, and ciphertexts, are treated as a system of two linear equations with two unknowns. By utilizing the principles of linear equations, the issue of cross-talk can be mathematically resolved. The proposed method enhances the security of the cryptosystem through the quantity and order of the keys. Specifically, the key space is significantly expanded by removing the requirement of uncorrected keys. This approach provides a superior method that can be easily implemented in various application scenarios.

Reversible Logic based Cryptography Design Algorithm using Random Keys

Reversible computations, besides quantum computing, have various applications in digital signal processing, nanotechnology and bioinformatics. They are particularly useful in designing low-power devices and improving computational efficiency. Cryptography is vital for protecting sensitive information in fields such as bioinformatics and digital signal processing where private data is frequently exchanged. However, cryptographic algorithms can consume significant power and require large areas, particularly when implemented in hardware. Reversible logic gates offer a potential solution by being more power-efficient and potentially reducing implementation area. Using random numbers as keys for both encryption and decryption in a reversible logic gate-based cryptographic algorithm can enhance security. LSB watermarking is a technique to embed additional metadata into digital media, improving data security. To evaluate the performance of the Field Programmable Gate Array for the Reversible Logic Gate Cryptography Design architecture, comparing it to the other state-of-the-art approach is necessary.

Watermarking and Encryption Technique to Avoid Data Leakage Based on Block Chain

Abstract: For end users to access various systems, services, and applications, multimedia data sharing is increasingly becoming an essential part of their daily lives. Cloud storage services in the real world frequently disclose data. In secure data transfer media, authenticity and copyright protection of multimedia content have always been a concern. As more people use the Internet and digital technologies, the issue has become more serious. Creating copyright protection, on the other hand, is more difficult and complicated. In proposed approach both Watermarking and Intermediary Re-encryption (PRE) approach used for productive mixed media content sharing. Watermarking is accustomed to concealing the data, for example, conceal privileged intel in computerized media like pictures. Data security is provided by encryption methods. In proposed work, secret key can be encoded utilizing encryption calculation with the assistance of key. The user's private key is then combined with the encrypted key information and can be embedded using LSB (Least Significant Bit) in the image or audio. The ECC Encryption algorithm can be used to encrypt an image or audio after secret information has been embedded. Finally, the embedded data verification process enables authorized users to extract the decryption key. When user information does not correspond with embedded information, illegal or unauthorized access can be identified. Furthermore, Block chain technology is used to safeguard these transaction details.

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Abstract: Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. First, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Second, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext.

Analysis of Key Storage Mechanism of Asymmetric Key-Related Functions in CNG Crypto Library

During the implementation of a crypto system, distributed cryptographic libraries are unable to endure situations where the execution environment rapidly changes because of a structural vulnerability that is by design. For this reason, Microsoft announced a CNG library to solve this problem; however, the CNG does not comprise verification tools regarding the execution results for developers, users, or experts to assess the crypto system. In addition, the CNG design means that it is difficult to ensure that the encryption or decryption keys can be found in real-time processing because almost all cryptographic functions are processed by handles. This paper analyzes the way that key information is found to assure the security of the implemented products or to debug them in the development process. For this reason, we analyze the key storage mechanism of asymmetric-key-related functions in the CNG library. This study provides more convenient ways to identify key-related information, such as debugging and evaluation, when a crypto system is implemented using the CNG library.