Encryption Features Research Articles

Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequential use of two components: homeomorphic data space transformation and synthetic data generation. Special attention is given to the privacy of image data. Specifics of image representation require special approaches towards encryption and synthetic image generation. We suggest use of (convolutional, variational) autoencoders and pre-trained feature extractors to enable applying privacy protection algorithms on top of the latent feature vectors captured from the images, and we updated the hybrid algorithms composed of homeomorphic transformation-as-encryption plus synthetic image generation accordingly. We show that an encrypted image can be reconstructed (by the pre-trained Decoder component of the convolutional variational autoencoder) into a secured representation from the extracted (by either the Encoder or a feature extractor) and encrypted (homeomorphic transformation of the latent space) feature vector.

Mobile Forensic Analysis of Signal Messenger Application on Android using Digital Forensic Research Workshop (DFRWS) Framework

Cybercrime is a crime committed using equipment connected to the internet. One of the cybercrimes that occured during the COVID-19 pandemic was the spread hoaxes about the COVID-19 vaccine which caused panic in society. Signal Messenger is one of the social media that has become a trending topic since the number of personal data security issues and the emergence of end-to-end encryption features. This research aims to find digital evidence on Signal Messenger application installed on the perpetrator's Android smartphone. This research uses Belkasoft, Magnet AXIOM, and MOBILedit Forensic Express tools and implements the Digital Forensics Research Workshop (DFRWS) framework in each stage of the research experiment. The research was carried out according to the case scenario with 11 predetermined parameters. Digital evidence is found from the Signal Messenger application: application information, account information, chat, pictures, videos, contacts, and stickers. The results of this research indicate that Belkasoft Evidence Center forensic tool is better, with an accuracy rate of 78.69%, while Magnet AXIOM is 26.23% and MOBILedit Forensic Express is 9.84%. The results of this research can be used as a reference for other forensic researchers/experts in handling similar crime cases on the Signal Messenger application to get better results.

Backup Encryption and Encrypted Backup Operation Performance in SQL Server

Database security features are important ceses to be focused on database system development. However, using security features can affect the system's performance. Encryption database backup is a kind of security algorithm option, that includes keys and certificates. It is essential for additional layer of security. This study aims to observe how Transparent Data Encryption (TDE) feature, as a solution tool for data security on Microsoft SQL Server, will impact the database performance. In this study, there will be an overview of database backup encryption and encrypted backup operation through backup testing in SQL Server. Hence, the research will be conducted on backup testing for monitoring database system's performance when implementing TDE solution. The study will contribute to the literature with using backup encryption algorithms in SQL Server in terms of efficiency and benchmarking of encrypted backup operation performance with TDE by finding how significant performance change is happening.

Securing an Information System via the SSL Protocol

The aim of the study is to improve the quality and level of security of an information system for monitoring cargo vehicles at production sites via modern encryption methods, which will provide for accurate management decisions in the event of a system breach. In accordance with modern requirements of information security policies, enhancement of information system security assumes the development of data protection concepts and the implementation of the most advanced encryption methods within the information system. The paper presents the authors’ solution for constructing an information system using SSL-based data encryption, SSL standing for the Secure Socket Layer. The characteristic feature of SSL-based encryption is the creation of a public-key cipher. This enables user and server authentication via digital signature technology. In addition, the method produces a session key that can be used to develop a fast symmetric cipher algorithm that allows encrypting of large arrays of information. Based on the proposed concept, the authors develop an information system for monitoring cargo vehicles at production sites that employs SSL-based encryption.

An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System

Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (>40), and GDD values exceeded (0.92). Key space is determined byu2sizeA0where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.

Viability of compensating employees in cryptocurrency – An exploratory study

Cryptocurrency is an emerging digital currency that revolutionized the transactional economy. It was valued at $2.2 trillion in April 2021, suggesting the widespread acceptance and usage of the currency. It penetrated numerous sectors including entertainment, infrastructure, healthcare, public sector, agriculture, financial services, travel, retail and information services. Recently these sectors started the practice of compensating employees in cryptocurrency (coins/tokens). This research explored the viability of compensating employees in crypto, across various components of compensation structure. This qualitative informative-exploratory research used the principles from the PRISMA model to screen 8,621 scholarly publications and reviewed 26 research publications and 23 companies using crypto for compensating employees. Content analysis and thematic analysis were conducted to report the findings. This research found that multinational organisations prefer to compensate global employees in crypto because of several advantages including ease of transfer, encrypted features, integration of blockchain technology, tax benefits, cost-saving and gain from crypto investments. Limiting factors such as the bartering nature of crypto, high volatility, tax regulations, limited investment avenues, resistance from employees and the market and concerns over its legality posed some doubts on its viability as a mode of payment. This study is of value to compensation and benefits experts, human resources professionals, finance professionals, scholars, and organisations who are interested in evolving technologies adding value to employees and businesses. This research added a new piece of knowledge concerning paying employees in crypto-currency that remained understudied until now.

Secure Content-Based Image Retrieval with Copyright Protection within Cloud Computing Environment

Every day, a tremendous amount of image data is generated as a result of recent advances in imaging and computing technology. Several content-based image retrieval (CBIR) approaches have been introduced for searching image collections. These methods, however, involve greater computing and storage resources. Cloud servers can address this issue by offering a large amount of computational power at a low cost. However, cloud servers are not completely trustworthy, and data owners are concerned about the privacy of their personal information. In this research, we propose and implement a secure CBIR (SCBIR) strategy for searching and retrieving cipher text image databases. In the proposed scheme, the extract aggregated feature vectors to represent the related image collection and use a safe Asymmetric Scalar-Product-Preserving Encryption (ASPE) approach to encrypt these vectors while still allowing for similarity computation. To improve search time, all encrypted features are recursively clustered using the k-means method to create a tree index. The results reveal that SCBIR is faster at indexing and retrieving than earlier systems, with superior retrieval precision and scalability. In addition, our paper introduces the watermark to discover any illegal distributions of the images that are received by unlawful data users. Particularly, the cloud server integrates a unique watermark directly into the encrypted images before sending them to the data users. As a result, if an unapproved image copy is revealed, the watermark can be extracted and the unauthorized data users who spread the image can be identified. The performance of the proposed scheme is proved, while its performance is demonstrated through experimental results.

THE METHOD OF INCREASING THE EFFICIENCY OF SYMMETRICAL BLOCK ENCRYPTION

Ensuring data safety and security are relevant in any sphere of human activity, which is directly related to the implementation of modern and advanced symmetric and asymmetric algorithms and methods of cryptographic protection of information. One of the well-known and safe encryption algorithms, known as AES (Advanced Encryption Standard), which is endowed with the properties of ease of practical implementation and high reliability, has found its wide application for block symmetric data encryption. A feature of symmetric encryption is the processing of large amounts of information, the use of the same key for encryption and decryption, high reliability of data protection. This paper proposes to improve this algorithm to provide faster data processing. All modifications of the AES transformations do not reduce the high cryptographic strength of the cipher, and their principles are described in detail in the paper. This practical implementation of the optimized algorithm shows the possibility of combining a number of mathematical operations that have a similar principle of processing elements, which allowed to reduce the processing time. In addition, in the improved implementation the MixColumns operation uses pre-calculated lookup tables of the required value, which also helps to increase the speed of transformations but at the cost of more memory for the code size. A comparative analysis of the practical implementation of the standard and optimized AES cryptoalgorithms has been carried out, and numerical indicators of the processing time of test data have been obtained, which are presented in the form of tables. Graphs of the dependence of the encryption and decryption procedure execution time on the file size for the standard and optimized AES algorithms have been constructed. The results show a significant percentage of optimization – up to 50% for encryption and up to 75% for decryption of data.

Privacy-Preserve Content-Based Image Retrieval Using Aggregated Local Features

Due to the recent improvements in imaging and computing technologies, a massive quantity of image data is generated every day. For searching image collection, several content-based image retrieval (CBIR) methods have been introduced. However, these methods need more computing and storage resources. Cloud servers can fill this gap by providing huge computational power at a cheap price. However, cloud servers are not fully trusted, thus image owners have legal concerns about the privacy of their private data. In this paper, we proposed and implemented a privacy-preserving CBIR (PP-CBIR) scheme that allows searching and retrieving image databases in a cipher text format. Specifically, we extract aggregated feature vectors to represent the corresponding image collection and employ the asymmetric scalar-product-preserving encryption scheme (ASPE) method to protect these vectors while allowing for similarity computation between these encrypted vectors. To enhance search time, all encrypted features are clustered by the k-means algorithm recursively to construct a tree index. Results show that PP-CBIR has faster indexing and retrieving with good retrieval precision and scalability than previous schemes.

A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning

One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic.

Random projection and hashing based privacy preserving for image retrieval paradigm using invariant and clustered feature

In recent years, exploration of local characteristics have been an effective feature extraction technique for managing multimedia repository for image retrieval. These class of approaches accelerate the image retrieval. However, with the increase of online intrusion, it is also essential to provide confidentiality for the private online data with effective functionality and significant retrieval accuracy. In this paper, we have solved the protuberant bottleneck problem of privacy-preserving in CBIR using random projection technique for feature encryption with effective retrieval. For this, we formulated highly discriminative interest points based features that are rotation and translation invariant and are constructed using k-means clustering and silhouette concept. Moreover, we have devised locally likely arrangement hashing technique that can incorporate fast, accurate, and secure matching over encrypted features. Based on the experiment, it substantially outperforms the recent state-of-the-art techniques in terms of retrieval accuracy and time overhead on various standard benchmarks, namely, Corel, Olivia, ALOI, and MPEG-7. The proposed method on average provided retrieval accuracy of 83.10%, 81.30%, 81.62%, 73.01% for Corel, Olivia, ALOI and MPEG-7 plaintexted feature, respectively while retrieval accuracy of 79.90% for Corel, 79.10% for Olivia, 79.97% for ALOI, and 67.34% for MPEG-7 using the encrypted feature.

BitCracker: BitLocker meets GPUs

BitLocker is a full-disk encryption feature available in recent Windows versions. It is designed to protect data by providing encryption for entire volumes and it makes use of a number of different authentication methods. In this paper we present a solution, named BitCracker, to attempt the decryption, by means of a dictionary attack, of storage units encrypted by BitLocker with a user supplied password or the recovery password. To that purpose, we resort to Graphics Processing Units (GPU) that are, by now, widely used as general-purpose coprocessors in high performance computing applications. BitLocker decryption process requires the computation of a very large number of SHA-256 hashes and also AES, so we propose a very fast solution, highly tuned for Nvidia GPU, for both of them. We analyze the performance of our CUDA implementation on several Nvidia GPUs and we carry out a comparison of our SHA-256 hash with the Hashcat password cracker tool. Finally, we present our OpenCL version, recently released as a plugin of the John The Ripper tool.

Design of a DFS to Manage Big Data in Distance Education Environments

Information technologies have invaded every aspect of our lives. Distance education was also affected by this phase and became an accepted model of education. The evolution of education into a digital platform has also brought unexpected problems, such as the increase in internet usage, the need for new software and devices that can connect to the Internet. Perhaps the most important of these problems is the management of the large amounts of data generated when all training activities are conducted remotely. Over the past decade, studies have provided important information about the quality of training and the benefits of distance learning. However, Big Data in distance education has been studied only to a limited extent, and to date no clear single solution has been found. In this study, a Distributed File Systems (DFS) is proposed and implemented to manage big data in distance education. The implemented ecosystem mainly contains the elements Dynamic Link Library (DLL), Windows Service Routines and distributed data nodes. DLL codes are required to connect Learning Management System (LMS) with the developed system. 67.72% of the files in the distance education system have small file size (<=16 MB) and 53.10% of the files are smaller than 1 MB. Therefore, a dedicated Big Data management platform was needed to manage and archive small file sizes. The proposed system was designed with a dynamic block structure to address this shortcoming. A serverless architecture has been chosen and implemented to make the platform more robust. Moreover, the developed platform also has compression and encryption features. According to system statistics, each written file was read 8.47 times, and for video archive files, this value was 20.95. In this way, a framework was developed in the Write Once Read Many architecture. A comprehensive performance analysis study was conducted using the operating system, NoSQL, RDBMS and Hadoop. Thus, for file sizes 1 MB and 50 MB, the developed system achieves a response time of 0.95 ms and 22.35 ms, respectively, while Hadoop, a popular DFS, has 4.01 ms and 47.88 ms, respectively.

DESSB-TRNG: A novel true random number generator using data encryption standard substitution box as post-processing

In addition to providing hardware-dependent costly and slow solutions, statistical weakness is an important disadvantage of a cryptographic TRNG that is tried to be removed with post-processing techniques. In this study, the use of Data Encryption Standard (DES) substitution-box (s-box)-based post-processing method is proposed as an alternative solution to the problem of a physical TRNG's inability to generate cryptographic randomness discussed in detail. The proposed s-box-based post-processing method is applied in real time to a TRNG architecture based on two different digital noise source scenarios such as jitter and metastability on the Altera Cyclone GX FPGA chip. In both scenarios, the statistical quality of raw random numbers that s-box based transformations of 48x32 (48-bit input 32-bit output) are applied in the post-process stage is improved to meet the cryptographic competencies. This contribution of the proposed method has been analyzed and confirmed by statistical test techniques such as bias, entropy, correlation, and uniformity as well as National Institute of Standards and Technology (NIST) SP 800-22 randomness evaluation tests. In addition to the statistical analysis, the security analysis of the proposed method has been made and a detailed literature comparison of TRNG is presented by measuring the hardware performance parameters for both scenarios. All these analysis results confirm that the proposed method produces functional results for TRNG by providing a better trade-off in terms of design parameters such as security, area-energy consumption, compression rate, simplicity-applicability and hardware cost. The study is significant to showing that s-boxes with strong mathematical encryption features can be used for different cryptographic aims, such as random number generation, outside of the confusion function.

Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography.

Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.

ПРИМЕНЕНИЕ ГЕНЕТИЧЕСКИХ АЛГОРИТМОВ В КРИПТОГРАФИИ

Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.

The affective pressures of WhatsApp: from safe spaces to conspiratorial publics

ABSTRACT In this paper we bring together media logics, affordances and affect theory to ask how conspiracy theory moves through WhatsApp groups and moves people through their encounters with these contents towards ‘conspiracy thinking’. Firstly, we draw upon media logic theory to examine the extent to which WhatsApp’s architecture, design and technical functions ‘steer’ users’ towards particular communication and behaviours. Second, we use affordance theory to examine how communities incorporate platform affordances into their tactics to resist, subvert or circumvent institutional power. Finally, we use theories of affect to understand whether the shared emotions and intensities that arise through encounters between digital environments and bodies of users drive experiences that bypass human cognition and representation to create ‘affective atmospheres’. We apply this integrated framework to ask whether WhatsApp’s closed infrastructure, end-to-end encryption and social features such as ‘Groups’ and the ‘forward’ button – with the embodied practices of users – shape affective environments which normalize conspiracy thinking.

Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.

Side channel attack on a partially encrypted MPEG-G file

New genome sequencing technologies have decreased the cost of generating genomic data, thus increasing storage needs. The International Organization for Standardization (ISO) working group MPEG has developed a standard for genomic data compression with encryption features. The approach taken in standard MPEG-G (ISO/IEC 23092) to compress genomic information was to group similar data into streams. Taking this into account, one of the protection options considered was to encrypt each stream separately. In this paper, we show that an attacker can use an unencrypted stream to deduce the encrypted content if streams are encrypted separately. To do so, we present two different attacks, one based on signal processing and the other one based on neural networks. The signal-based attack only works with unrealistic settings, whereas the neural network-based one recovers data with realistic settings (regarding read length and coverage). The presented results made MPEG reconsider the encryption strategy, before final publication of the standard, discarding separate streams encryption approach.

Computational intelligence based secure three-party CBIR scheme for medical data for cloud-assisted healthcare applications

Medical images with various modalities have become an integral part of the diagnosis and treatment of several diseases. The medical practitioners often use previous case studies to deal with the current medical condition of any particular patient. In such circumstance, they need to securely access medical images of various cases which are generally stored in a network and are vulnerable to malicious attacks. To address these sensitive inadequacies, we have proposed computational intelligence based secure healthcare Content based Image Retrieval (CBIR) for medical image retrieval scheme through which any medical practitioner can retrieve the image in an encrypted domain in cloud environment. In this regard, hamming distance-based similarity matching is the only available technique that effectively handles the comparison between encrypted features. This technique requires binary features to perform similarity matching, and the performance of such features in image retrieval is poor. In this concern, we have suggested a salient component-based binary feature extraction approach to enhance retrieval accuracy. Initially, we have re-arranged the input image using the saliency map, principal texture direction, and entropy to place the salient components at the starting blocks. Subsequently, we have employed a block-level majority voting scheme on the salient blocks of the image to obtain local binary features. As a result, the final feature vector carries more features from the salient part of the image, which propitiously improves the retrieval accuracy. Later, we have encrypted the binary feature vector and performed image retrieval on cloud environment which involve Data Owner, Database Service Provider and Client over encrypted domain to full fill the security aspect. Finally, we have used medical as well as Corel image datasets to validate the retrieval performance accuracy of the proposed scheme. The experimental results obtained from real life datasets exhibit that the proposed method is secure and provides comparable retrieval accuracy concerning other related schemes in the domain.