Key Retrieval Research Articles

Gid Crypto: Application for End-to-End Encrypt and Decrypt E-mail and Data

Privacy and security in our digital environment have become essential today. Some data stored on hard disks, in flash memory, or exchanged through our communication channels is sensitive and should be kept confidential. Additionally, documents containing confidential information, such as business algorithms or new application coding, require adequate measures to ensure privacy and security. Therefore, data encryption is crucial in protecting information from being intercepted, misused, or stolen. While encryption tools are available for business e-mails, using these tools often requires subscribing as a member and paying extra fees. Effective tools are not limited to e-mail alone. Still, they can also be applied to any text data that requires a high level of security and complexity to ensure the protection of the data for a duration longer than it needs to be safeguarded. Our research aims to develop a stand-alone application that encrypts data to ensure its confidentiality without additional subscriptions or fees. The application is designed to be user-friendly, with fast encryption and decryption capabilities. It can be used not only for encrypting and decrypting e-mail messages but also for programming languages and Excel data. The application development utilizes techniques such as Diffe-Hellman, Mersenne Twister, and AES, arranged in layers to generate complex ciphertext that is difficult to decrypt without the developed application. LabVIEW was used for testing encryption speed. The attempt to attack encrypted data was subjected, but the result yielded no successful key retrieval.

Cryptanalysis of the SHMW signature scheme

In recent research, Durandal, a signature scheme based on rank metrics following Schnorr's approach, was introduced to conceal secret key information by selectively manipulating the vector subspace of signatures. Later, an enhancement, namely the SHMW signature scheme, with smaller keys and signatures while maintaining EUF-CMA security, was proposed. Both Durandal and SHMW require adversaries to solve hard problems (i.e., Rank Support Learning, Rank Syndrome Decoding, and Affine Rank Syndrome Decoding) for secret key retrieval, in which the parameters are designed to withstand at least 128-bit computational complexity. The authors claimed that the security of the SHMW scheme is deemed superior to that of the original Durandal scheme. In this paper, we introduce a novel approach to identifying weak keys within the Durandal framework to prove the superiority of the SHMW scheme. This approach exploits the extra information in the signature to compute an intersection space that contains the secret key. Consequently, a cryptanalysis of the SHMW signature scheme was carried out to demonstrate the insecurity of the selected keys within the SHWM scheme. In particular, we proposed an algorithm to recover an extended support that contains the secret key used in the signature schemes. Applying our approach to the SHMW scheme, we can recover its secret key with only 97-bit complexity, although it was claimed that the proposed parameters achieve a 128-bit security level. The results of our proposed approaches show that the security level of the SHMW signature scheme is inferior compared to that of the original Durandal scheme.

Retrieval, repetition, and retention: unveiling vocabulary acquisition strategies for ESL learners

This paper explores the practical implementation of retrieval practice techniques for improving English as a Second Language (ESL) vocabulary acquisition. Effective strategies for fostering long-term retention and comprehension of vocabulary are of utmost importance in the field of ESL education. Three key retrieval practice techniques, namely, The Brain Dump, Low-Stakes Quizzes, and Flashcards, were examined, and their advantages, drawbacks, and potential impact on ESL learners were discussed. Additionally, the integration of digital technology with traditional retrieval practice tools is discussed, highlighting the evolving landscape of language learning strategies. This discussion emphasizes the significance of retrieval practice in enhancing ESL vocabulary acquisition, offering educators and learners valuable tools to reinforce memory traces, engage in active recall, and promote metacognition. However, challenges, such as time constraints, test anxiety, and rote memorization, must be carefully considered in their implementation. Overall, this paper provides insights into the practical utilization of retrieval practice techniques, offering educators a nuanced perspective on improving ESL learners' long-term vocabulary retention and comprehension.

Bio-Hash Secured Hardware e-Health Record System.

Dual securing strategy for all-hardware e-Health Record System is designed and developed for improved security and reduced Hardware Execution Time (HET). A compact novel Hashed Minutiae Random Fusion (HMRF) logic enables to achieve high irreversibility and increased non-reconstruction capability of the bio-template based Bio-Hash key. AES encryption of the patient's health data during Write mode and decryption during View mode are seamlessly performed through the lively generated key, yielding low HET through optimized slack. On the other hand, biometric controlled key retrieval during Read only mode for a single user access is performed on the pre-scrambled Bio-Hash key, to enable bypassed decryption (direct) of the Patient's health data for self-review. The proposed pseudo cascaded SHA-3 (Secured Hash Algorithm) architecture being the first stage in HMRF, hashes the biometric minutiae of both Patient (P) and Medical Practitioner (MP) with low Latency. Thus, facilitating in further lowering of the HET by reducing the clock count by one. The subsequent Random Compression Logic (RCL) skims the hashed value from 512 to 128 bits along with the help of priority compression logic (PCL) to achieve reduced bits handling thereby lowering the Power budget. Four fusion modes are leveraged to achieve better randomization and non-recoverability. Implementation of this HMRF logic on Virtex-7 (V7) FPGA device has yielded low Area of 4191 slices. Lesser Area of 11.6% is observed for this HMRF module compared to the reported design, excluding level shifter and PCL. Further, low HETs of 8.2/8.3/8.0 ns during Write/View/Read only modes respectively are being noticed. The dynamic Power dissipated for the three modes of operations are found to be 1.418/1.420/0.676 watts respectively.

A New Remote Sensing Image Retrieval Method Based on CNN and YOLO

<>Retrieving remote sensing images plays a key role in RS fields, which activates researchers to design a highly effective extraction method of image high-level features. However, despite the advanced features of the image can be extracted by deep-learning method, features fail to cover the overall information due to its rich and complex background. Extract key regions from RS images, recede background interference and retrieval accuracy needs to be solved. Combined YOLOv5 target recognition algorithm with deep-learning method, this paper proposes a novel retrieval method based on target critical region detection. Firstly, the key retrieval regions have been identified. YOLOv5 target recognition algorithm has been used to identify the key regions of the image and served as the retrieval regions. Secondly, the retrieval characteristics are determined. Combining with the CNN model ResNet50, the retrieval features are extracted from the retrieval regions acquired in the previous step, in addition, PCA method has been used to reduce the dimension of the retrieval features. Finally, using weighted distance based on class probability to measure the similarity between a query and retrieve images. Experimental results show that the proposed method can extract better image retrieval features and improve the retrieval performance of RS image.<>

Biblio-metrology analysis on clinical application of Chengshan (BL57) in ancient Chinese medical literature

To analyze and sum up clinical application regularities of Chengshan (BL57) acupoint in ancient Chinese literature. The book Zhonghua Yidian (Collection of Chinese Medical Classic Works, 5th edition) containing 1 156 ancient medical books was used as the retrieved literature source. The key retrieval words used were BL57 acupoint related indications, prescriptions, treatment methods and taboos, followed by sorting out relevant articles and establishing a database. A total of 196 articles meeting our inclusion standards of the present study, involving 61 ancient books which contain 174 related terms covering 56 diseases or syndromes of internal medicine, surgery, pediatrics, ophthalmology and otorhinola-ryngology, etc.. Among them, the most frequently attending disease treated by single BL57 or acupoints combination was limb meridian disorders, and the most frequently used auxiliary acupoint was Kunlun (BL60). The most frequently used auxiliary meridian was the Bladder Meridian of Foot Taiyang. In addition, a total of 71 terms involved the acupuncture and moxibustion techniques of BL57, covering 7 treatment methods. In terms of the treatment methods, the dose of moxibustion was five cones in each session, and the depth of acupuncture needle insertion was about seven fen. BL57 is widely used in various clinical departments, especially for limb meridian diseases.

A portable hardware security module and cryptographic key generator

It has been noted with concern that the ability of a password to keep an information system secure is diminishing. Increasingly sophisticated attack vectors and low memorability associated with complicated passwords are among the leading reasons limiting security provisioned by passwords. Cryptographic keys suffer from issues including lack of memorability, vulnerable storage mechanisms, key retrieval attacks, lockouts due to key loss and risk of using the same key for multiple services. This study proposes a novel Hardware Security Module (HSM) as a basis for the generation/ re-creation of cryptographic keys. The designed hardware module entirely eliminates the stored cryptographic keys thus eliminating attacks against stored keys. The HSM derives the cryptographic key from sub-components behaving similar to multi-factor authentication, where each factor is an independent authenticator. The proposed scheme enhances security by incorporating physical security into digital security, i.e. as long as either the crypto provider device remains secure or the human component remains secure, the system security remains intact. The scheme proposes a strategy based on defense in depth to secure the HSM, its user, the related service from attacks ranging from simple shoulder surfing to sophisticated Man-in-the-Middle attacks. The proposed HSM is based on commodity hardware components thus having limited cost implications.

SPEEDY Quantum Circuit for Grover’s Algorithm

In this paper, we propose a quantum circuit for the SPEEDY block cipher for the first time and estimate its security strength based on the post-quantum security strength presented by NIST. The strength of post-quantum security for symmetric key cryptography is estimated at the cost of the Grover key retrieval algorithm. Grover’s algorithm in quantum computers reduces the n-bit security of block ciphers to n2 bits. The implementation of a quantum circuit is required to estimate the Grover’s algorithm cost for the target cipher. We estimate the quantum resource required for Grover’s algorithm by implementing a quantum circuit for SPEEDY in an optimized way and show that SPEEDY provides either 128-bit security (i.e., NIST security level 1) or 192-bit security (i.e., NIST security level 3) depending on the number of rounds. Based on our estimated cost, increasing the number of rounds is insufficient to satisfy the security against quantum attacks on quantum computers.

Compressive Imaging Encryption with Secret Sharing Metasurfaces

AbstractExploiting various degrees of freedom of light, metasurfaces have unique advantages in multiple‐channel information storage and demonstration, which thereby provides a novel platform to convey the keys and cipher images for different encryptions. Following the secret sharing principle of visual cryptography (VC), the authors here successfully embed both the keys and cipher images of computational ghost imaging (CGI) encryption into the holographic metasurface‐images (meta‐images). The decryption process starts with key retrieval via optical observation of overlapped meta‐images, followed by a compressive CGI calculation to reconstruct the target images according to the obtained key and steganographic cipher images with a high compression ratio of 4. By integrating metasurface imaging, VC, and CGI, the authors’ proposed encryption scheme exempts conventional key distribution and transmission of CGI, enhances the security by secret sharing of VC, and increases the amount of hiding data contained in meta‐images with compressive sensing.

SBCMA: Semi-Blind Combined Middle-Round Attack on Bit-Permutation Ciphers With Application to AEAD Schemes

Side-channel and fault injection attacks are well-researched topics within physical security of cryptographic implementations. They can reduce the complexity of the key retrieval to trivial numbers. It was shown that their combination is even more effective, for example in breaking redundancy countermeasures. In this paper, we present the first semi-blind combined attack that allows secret key retrieval in unknown plaintext and ciphertext scenario. SBCMA – Semi-Blind Combined Middle-round Attack is aimed at bit-permutation ciphers, such as GIFT-128 which serves as a case study for this work. On average, it can recover GIFT-128 master key with 92.17 encryptions and 91.17 faults. For GIFT-128 based NIST LWC Round 2 candidates, SBCMA requires 13.79 sessions and 94.32 faults on average.

Deep face fuzzy vault: Implementation and performance

Biometric technologies, especially face recognition, have become an essential part of identity management systems worldwide. In deployments of biometrics, secure storage of biometric information is necessary in order to protect the users’ privacy. In this context, biometric cryptosystems are designed to meet key requirements of biometric information protection enabling a privacy-preserving storage and comparison of biometric data, e.g. feature vectors extracted from facial images. Until now, biometric cryptosystems have hardly been applied to state-of-the-art biometric recognition systems utilizing deep convolutional neural networks.This work investigates the application of a well-known biometric cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors extracted through deep convolutional neural networks. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep feature vectors to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted. At key binding, obtained feature sets are locked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency of different polynomial reconstruction techniques is investigated. The proposed feature transformation method and template protection scheme are agnostic of the biometric characteristic and, thus, can be applied to virtually any biometric features computed by a deep neural network. In experiments, an unlinkable improved deep face fuzzy vault-based template protection scheme is constructed employing features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). For the best configuration, a false non-match rate below 1% at a false match rate of 0.01%, is achieved in cross-database experiments on the FERET and FRGCv2 face databases. On average, a security level of up to approximately 28 bits is obtained. This work presents an effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face.

A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher

This paper presents the employment of a DPA attack on the NIST (National Institute of Standards and Technology) standardized AES (advance encryption standard) protocol for key retrieval and prevention. Towards key retrieval, we applied the DPA attack on AES to obtain a 128-bit secret key by measuring the power traces of the computations involved in the algorithm. In resistance to the DPA attack, we proposed a countermeasure, or a new modified masking scheme, comprising (i) Boolean and (ii) multiplicative masking, for linear and non-linear operations of AES, respectively. Furthermore, we improved the complexity involved in Boolean masking by introducing Rebecca’s approximation. Moreover, we provide a novel solution to tackle the zero mask problem in multiplicative masking. To evaluate the power traces, we propose our custom correlation technique, which results in a decrease in the calculation time. The synthesis results for original implementation (without countermeasure) and inclusion of countermeasure are given on a Zynq 7020 FPGA (Artix-7 device). It takes 424 FPGA slices when implemented without considering the countermeasure, whereas 714 slices are required to implement AES with the inclusion of the proposed countermeasure. Consequently, the implementation results provide the acceptability of this work for area-constrained applications that require prevention against DPA attacks.

Deep-learning-based ciphertext-only attack on optical double random phase encryption

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in real-time from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

Finding the Needle in the Haystack: Metrics for Best Trace Selection in Unsupervised Side-Channel Attacks on Blinded RSA

For asymmetric ciphers, such as RSA and ECC, side-channel attacks on the underlying exponentiation are mitigated by countermeasures like constant-time implementation and blinding. This restricts an attacker to a single side-channel trace for an attack as a different representation of the private key is used for each exponentiation. In this work, we propose an unsupervised machine learning framework for side-channel attacks on asymmetric cryptography that analyzes leakage in multiple side-channel traces, identifying the best trace for key retrieval. We apply Principal Component Analysis (PCA) preprocessing followed by a classification step that assigns segments of traces to elementary operations of the Square and Multiply exponentiation of RSA. In order to estimate the attack complexity for each trace in terms of key enumeration effort, we introduce two new metrics: The Entropy-based Cost Function (EBCF) is used to select a trace for the attack as well as bits which have to be brute-forced if not all bits can be determined correctly from this single trace. To reduce brute-force complexity further, we introduce Illegal Sequence Detection (ISD) to remove brute-force candidates which do not fit to the Square-and-Multiply scheme. We first provide a proof of concept for 320-bit key length traces and, moving towards a more realistic scenario, retrieve the key from a 1024-bit RSA implementation protected by message and exponent blinding. We are able to select the trace with the least remaining brute-force complexity from 1000 power measurements of the signature generation with randomized inputs and blinding values on a 32-bit ARM Cortex-M4 microcontroller.

Orthogonal obfuscation based key management for multiple IP protection

With the development of system-on-chip (SoC) chips, more and more design houses are cooperating with each other's. How to achieve benefit sharing and key management for multiple intellectual properties (IPs) has become an emergency problem. This work proposes an orthogonal obfuscation method to protect multiple IPs. The proposed method permits cooperators to control the project using different security keys, protects the patterns with an orthogonal key, and be convenient for the user to manage the key. For reasons of increased security, the proposed method hides the original keys to prevent information leakage. Multi-port Physical Unclonable Function (MPUF) circuit is used as the additional orthogonal key to cluster membership. It protects the IPs from hardware attacks such as brute-force attack, member leakage attack, reverse engineering and so on. The security analyzes results show that the proposed method reduces the key retrieval time by 36.3% over the baseline. The proposed obfuscation methods have been successfully applied to ISCAS′89 benchmark circuits and cryptographic algorithms. Experimental results indicate that the orthogonal obfuscation only increases the area by 3.43% and consumes 2.77% more power than the baseline.

The Security Enhancement of Symmetric Key Crypto Mechanism based on Double Stage Secret Model

ABSTRACT All smart services in cloud computing platforms have a data-sharing process to execute the task. Moreover, several crypto techniques were implemented to provide efficient and secure data transmission channel. However, it has been suffered with a number of issues because of several harmful attacks and unauthenticated key retrieval. Thus in the wireless medium, transferring the data amid two users are challengeable task. To overcome this issue, a novel Double-Stage Secret (DSS) cryptosystem is proposed to prevent the attacks in the communication channel, here the encryption and decryption function is processed based on some specific condition. If the DSS condition is satisfied then the client or user is requested to decrypt the message. To validate the secure and success rate of the proposed model, an efficient novel back sniff is developed and launched in the data transmission channel and its secure range is evaluated. In addition, the simulation of this research is done by Java, running on net beans IDE 8.2 in windows 10 platform and the results illustrated that the proposed model has attained high performance by gaining a high secure rate and less processing time of encryption and decryption than the existing algorithms.

Test the Effectiveness of Infusion Formulation of Tamarind Fruit (Tamarindus indica) with Temu Kunci (Boesenbergia Rotunda) Rhizome against Head Lice Mortality (Pediculus Humanus Capitis)

Pediculosis capitis is an infectious disease caused by the parasite infestation Pediculosis humanus capitis. It is necessary to do alternative medicine by using natural ingredients as insecticides to kill head lice, one of which is tamarind fruit (Tamarindus indica) and temu Kunci rhizome (Boesenbergia rotunda) which have chemicals that work as insecticides. The form of the research is experimental in nature at the Laboratory of Clinical Parasitology, University of Muhammadiyah Sidoarjo. The sample used was head lice obtained as many as 480 tails from santriwati Pondok Pesantren Nurul Ikhlas Sidoarjo tested with tamarind fruit and rhizome of Temu Kunci obtained from the Probolinggo area, East Java. Samples were tested with 6 treatments, namely 100% tamarind fruit; 25% tamarind and 75% meeting lock; 50% tamarind and 50% meeting lock; 75% tamarind and 25% meeting lock; 100% key retrieval; negative control (aquades) and 4 repetitions. The data obtained for 24 hours were analyzed by using the One Way ANOVA statistical test and continued with the LSD test. One Way ANOVA test obtained a significance value of P= 0.000 (p<0.005) and in the LSD further test, the most effective infusion solution was obtained at K4 (75% tamarind fruit and 25% rhizome of Temu Kunci). The conclusion is that there is an effectiveness of infusion formulation of tamarind fruit (Tamarindus indica) with temu lock rhizome (Boesenbergia rotunda) which can eradicate head lice (Pediculus humanus capitis).

Strength in Numbers: Improving Generalization with Ensembles in Machine Learning-based Profiled Side-channel Analysis

The adoption of deep neural networks for profiled side-channel attacks provides powerful options for leakage detection and key retrieval of secure products. When training a neural network for side-channel analysis, it is expected that the trained model can implement an approximation function that can detect leaking side-channel samples and, at the same time, be insensible to noisy (or non-leaking) samples. This outlines a generalization situation where the model can identify the main representations learned from the training set in a separate test set.This paper discusses how output class probabilities represent a strong metric when conducting the side-channel analysis. Further, we observe that these output probabilities are sensitive to small changes, like selecting specific test traces or weight initialization for a neural network. Next, we discuss the hyperparameter tuning, where one commonly uses only a single out of dozens of trained models, where each of those models will result in different output probabilities. We show how ensembles of machine learning models based on averaged class probabilities can improve generalization. Our results emphasize that ensembles increase a profiled side-channel attack’s performance and reduce the variance of results stemming from different hyperparameters, regardless of the selected dataset or leakage model.

Information Extraction from Electronic Medical Records using Natural Language Processing Techniques

Patients share key information about their health with medical practitioners during clinic consultations. These key information may include their past medications and allergies, current situations/issues, and expectations. The healthcare professionals store this information in an Electronic Medical Record (EMR). EMRs have empowered research in healthcare; information hidden in them if harnessed properly through Natural Language Processing (NLP) can be used for disease registries, drug safety, epidemic surveillance, disease prediction, and treatment. This work illustrates the application of NLP techniques to design and implement a Key Information Retrieval System (KIRS framework) using the Latent Dirichlet Allocation algorithm. The cross-industry standard process for data mining methodology was applied in an experiment with an EMR dataset from PubMed todemonstrate the framework. The new system extracted the common problems (ailments) and prescriptions across the five (5) countries presented in the dataset. The system promises to assist health organizations in making informed decisions with the flood of key information data available in their domain. 
 Keywords: Electronic Medical Record, BioNLP, Latent Dirichlet Allocation

Long-term behavioural rewriting of maladaptive drinking memories via reconsolidation-update mechanisms.

Alcohol use disorders can be conceptualised as a learned pattern of maladaptive alcohol-consumption behaviours. The memories encoding these behaviours centrally contribute to long-term excessive alcohol consumption and are therefore an important therapeutic target. The transient period of memory instability sparked during memory reconsolidation offers a therapeutic window to directly rewrite these memories using targeted behavioural interventions. However, clinically-relevant demonstrations of the efficacy of this approach are few. We examined key retrieval parameters for destabilising naturalistic drinking memories and the ability of subsequent counterconditioning to effect long-term reductions in drinking. Hazardous/harmful beer-drinking volunteers (N = 120) were factorially randomised to retrieve (RET) or not retrieve (No RET) alcohol reward memories with (PE) or without (No PE) alcohol reward prediction error. All participants subsequently underwent disgust-based counterconditioning of drinking cues. Acute responses to alcohol were assessed pre- and post-manipulation and drinking levels were assessed up to 9 months. Greater long-term reductions in drinking were found when counterconditioning was conducted following retrieval (with and without PE), despite a lack of short-term group differences in motivational responding to acute alcohol. Large variability in acute levels of learning during counterconditioning was noted. 'Responsiveness' to counterconditioning predicted subsequent responses to acute alcohol in RET + PE only, consistent with reconsolidation-update mechanisms. The longevity of behavioural interventions designed to reduce problematic drinking levels may be enhanced by leveraging reconsolidation-update mechanisms to rewrite maladaptive memory. However, inter-individual variability in levels of corrective learning is likely to determine the efficacy of reconsolidation-updating interventions and should be considered when designing and assessing interventions.