Authentication Database Research Articles

Mult-Pattern Fingerprint Security System By IJISRT

To improve security measures, a multi-pattern fingerprint security system is proposed in this study. To offer sophisticated security features, the suggested system combines a database, an Arduino Uno board, and a fingerprint sensor. Because of the way the system is designed, it may keep multiple fingerprint patterns in the database, which adds to its adaptability and versatility. The Arduino Uno board functions as the client in the proposed system's client-server architecture, while the database serves as the server. The user's fingerprints are captured by the fingerprint sensor, which then transmits the information to the Arduino Uno board for processing. After that, the Arduino Uno board transmits the fingerprint information to the database for authentication and storage. Additionally, the system has a graphical user interface (GUI) that.

Secure Big Data Storage on Multi-Cloud Servers with Enhanced Attribute-Based Access Control

Abstract: Data outsourcing is now subject to additional safety and privacy concerns as a result of the rise in cloud computing demand. Because data management was moved to words an unstable cloud during the outsourcing stage of the data, data permit control identified a key problem in the cloud repository. This analysis uses the Attribute-Based Access Control method to control access to the cloud storage to solve the issue. The data is reserved in the cloud and protected by strict security before the user may access it. This model takes into account specific properties of the cloud data stored in the database authentication process in order to store the information of registered collections alongside the user-committed data. Each of the clusters has a unique set of benefits, including usernames, group names, and registry message authorization codes. Prior to sending the data to the service provider, it should be encrypted to ensure its security. However, there may occasionally be problems with the supplier’s security measures. In this outcome, a variety of metrics are analyzed, such as the amount of time used for encryption, decryption, key generation, and time consumption. The numerous existing techniques, like Nth Truncated Ring Units and Cipher text. Constraints on online storage access may make policy attribute-based encryption impossible. The time of encryption is reduced by 20% and 36%, respectively, via NTRU and CP-ABE. The proposed technique decrypts data 10.64% faster than the current approach, which decrypts data 19% more slowly.

Human retinal biometric recognition system based on multiple feature extraction

Retinal biometric trait is one of the safest and most secure biometric traits that can be used for personal identification and authentication. The poor contrast in captured retinal images initially requires the adoption of a preprocessing technique, especially when the vessel segmentation is required. As a result, an effective preprocessing technique is employed before the feature extraction process. The reliability of the segmentation depends on the consistency of the contrast across the images. Due to the complexity of the retinal vascular patterns, segmenting the vessels manually is a time-consuming task that also requires extensive training and abilities to distinguish the thinner blood vessel pixels from the background of the retinal images. We present a unique blood vessel segmentation technique based on Radial Chebyshev Moments (RCMs) shape descriptors. Moment-based shape descriptors are typically strong, concise, and simple to compute. They are also insensitive to the object’s scale, rotation, and translation. Furthermore, to eliminate the eye movement and to overcome the various lighting problems, the following multiple features are extracted in the feature extraction stage to accurately authenticate a person: statistical (gray-level) features, shape-based (RCM) features, and structural features, such as minutiae, bifurcation points, optical disc, and enclosed regions. The principal components analysis methodology is used to optimize the feature vector, and the target image is identified using the support vector machine classifier. This step helps to determine whether the test image belongs to the authorized person or not. RIDB and VARIA are the two standard retinal authentication databases and DRIVE and STARE are the two general retinal databases that are used for evaluating the performance of the system. The system yielded a high identification rate of 98%, 97.5%, 97.2% and 94.4% with average processing time of 0.987 s, 0.40 s, 1.262 s and 0.65 s, respectively. From the results, it is evident that the proposed method is more effective and efficient for authenticating individuals.

Water Level Monitoring System Simulation Using Flutter Framework in Pekalongan City

Pekalongan City is a region on the northern coast of Java with a sloping topography and is susceptible to the increasing sea level, which can result in tidal floods. As previous research revealed, ten villages in three districts, including coastal areas, are vulnerable to tidal floods. Moreover, the vulnerability analysis based on BNPB Head Regulation (Perka) No. 2 of 2012 unveiled that seven out of ten villages in three districts have a high risk of tidal floods. Pekalongan City experienced a tidal flood that swamped 16 villages in three districts at the beginning of 2021. It was the result of heavy rains and strong tides along the northern shore. The tidal flood caused damage to a variety of sectors, including agriculture, communities, infrastructure, and clean water sanitation. Information technology development was employed in disaster management, making it simpler to acquire data for predicting future disasters, obtaining data in disaster-affected areas, and implementing mitigation in disaster-prone areas. This study collected data through interviews and a literature review. This research created Android applications simulating a water level monitoring system using the flutter framework. Firebase was utilized as a database for authentication, while Google Maps Platform was deployed to display maps during the application simulation. It is anticipated that the simulation of the water level monitoring system will be the first step in building the water level monitoring system in Pekalongan City. Hence, residents of Pekalongan City can utilize it to assist with mobility issues during floods.

Data Matrix Based Low Cost Autonomous Detection of Medicine Packages

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.

A novel zero-watermarking algorithm based on robust statistical features for natural images

In this paper, we develop a robust image zero-watermarking algorithm based on multiple circular statistical features. First of all, we detect stable feature points from the host image, and construct circular region partitions from the identified feature points, which contain the critical information of the host image. Second, the statistics and differences of circular region partitions are calculated, which contain both the local differences and the global information of the host image. Third, we apply discrete cosine transform (DCT) and singular value decomposition (SVD) in statistics and differences in order to construct the feature image. The stability of DCT and SVD further warrants the robustness of the algorithm. Moreover, Arnold transform is used to scramble the watermark image to enhance the security of the algorithm. Finally, we obtain the zero watermark by performing the exclusive-or (XOR) operation between the feature image and the scrambled watermark image, and the zero watermark is stored in the copyright authentication database for copyright authentication. Moreover, to combat geometric attacks, we develop a blind image correction algorithm without using the original image to accurately correct the attacked image. Numerous experiments and comparisons with the state-of-the-art (SOTA) watermarking algorithms confirm that the proposed algorithm affords good robustness against various attacks, such as filtering attacks, noise attacks, geometric attacks, and even a random combination of the above-stated attacks.

GRAPHICAL IMAGE PASSWORD AUTHENTICATION USING JARO DISTANCE

This research proposes a new authentication mechanism called Graphical Image Password Authentication Using Jaro Distance. A user can register five different related photographs using the suggested technique. To identify the similarity of the five pictures, a Gaussian mixture and the Jaro Distance method are used to preprocess the provided five similar images. All five photographs will be saved in the System database if they are comparable. The user connects in using their permitted information and a picture password based on a drawing. The password is processed by using a Gaussian mixture and implemented using the Jaro Distance technique to compare with the registered passwords in the database for authentication. The suggested method is tested with some example input image passwords, and the performance is evaluated using recall and precision measures. By ensuring authentication for user security, the proposed effort might achieve an accuracy of over 87%.

A novel multimodal hand database for biometric authentication

A novel multimodal hand database for biometric authentication

Authentication of fish species served in conveyor-belt sushi restaurants in Taiwan using DNA barcoding

Fish fraud is a problem worldwide. Not only does it erode customer confidence, but it also jeopardizes public health. Typically, it is difficult to authenticate fish species identity based on morphological characters after food processing. Fortunately, DNA is relatively resistant to processing methods, and DNA barcoding has proven useful for species identification. Although fishery product mislabeling is recognized as an issue in Taiwan, a respective study on sushi has not yet been conducted. Here, we successfully DNA-barcoded 121 out of 122 sushi samples from eleven chain restaurants of the conveyor-belt type in Taiwan, revealing an overall mislabeling rate of ~17.36%. We found that capelin roe is commonly used to replace other fish roe, such as herring. Tuna, which is susceptible to fraud, was always correctly labeled. We also noted inconsistent usage of Chinese common names, conflict between Chinese and English names, and ambiguous usage of Japanese kanji in Chinese labels, all serving to confuse species identity. Moreover, spurious reference sequences in the BOLD database and the lack of a standard list of common names of fishes with their corresponding scientific name both impede investigations of fish substitution. We advocate that the responsible authorities establish a reliable barcoding database for molecular authentication of fishes, publish a respective standard for common and scientific names, and legislate for more comprehensive labeling regulations based on experience in other jurisdictions to facilitate mislabeling investigations and to deter food fraud. • A high successful rate in amplifying full-length DNA barcodes from sushi samples. • “Correctly” labeled items may not have matched customer expectations. • A reliable barcoding database is required. • A “Taiwanese Fish Names Standard” is appealed to be constructed. • A comprehensive labeling regulations is needed for food fraud investigations.

Efficient boolean SSE: A novel encrypted database (EDB) for biometric authentication

Biometric authentication is up-and-coming to replace the traditional identity authentication method (e.g., passwords, PIN, identification cards) for its convenience and intelligence. With more and more users using this method, the database becomes more extensive, and the functions are seriously challenged. Data outsourcing has advantages in terms of convenience and cost savings, so it has attracted much research effort. However, due to the biometric's immutability of the whole life, it is extremely sensitive, and disclosing it to a third party is undesirable. In this paper, we address the issue of securely outsourcing biometric database. We propose a novel boolean searchable symmetric encryption (SSE) to construct a secure interactive protocol when outsourcing. A new encrypted database construction method was proposed, using the more efficient boolean vectors. Based on this, We suggest three kinds of expressive SSE, supporting disjunctive query, boolean query, and lightweight settings. We prove the schemes' correctness and security theoretically. Our constructions use simple cryptographic tools, such as symmetric cryptography and pseudo-random functions. They are straightforward to understand and easy to implement. The experiments show that all our schemes are practical and more efficient than the existing methods.

Repeatable Data Hiding: Towards the Reusability of Digital Images

This article proposes a repeatable data hiding framework for digital images, in which the distortion caused by data hiding is invariable no matter how many times the embedding operation is executed. As a result, the usability of images can be always guaranteed. To achieve repeatable data hiding, we deduce the theoretical modification probabilities of the elements in cover image (an image used for data hiding). Then we design an embedding framework to make the practical modification probabilities of cover elements equal to the deduced ones. When additional data is embedded into cover image with the deduced probabilities, the modification-trace of current embedding is replaced by the latter embedding. That means multiple embeddings do not produce new modification-trace, so that the distortion caused by data hiding is invariable. Therefore, the repeatability of data hiding can be guaranteed. Finally, we describe some applications of repeatable data hiding, e.g., logistics management, authentication database management, and steganography, to show the practicability of our framework.

Authentication Database Leakage Detection

Authentication is the first step for any important applications. Password verification is widely employed in the Internet. Some advanced mechanisms including bio features, such as fingerprints or retina. However, digital password authentication credential is commonly used in the public. In other words, traditional password verification is still an important authentication credential mechanism for today's online services. Password database may be compromised. Once if the database is stolen, it is dangerous and critical for any services. Consequently, password database leakage becomes an important issue. Detection and countermeasure are essential for such a disaster. Our research proposing a scheme based on a authentication database storage mapping method to detect possible leakage of authentication database. By multiple mapping mechanism, if the authentication database is stolen, an attacker could not know any user's correct password. This method can reduce additional space of storing passwords with ability to detect security event for stolen authentication databases. Keywords:Application Security; System Security; Leakage Detection; Authentication database, Password DOI: 10.7176/NCS/12-01 Publication date: January 31 st 2021

Prediction-based robust blind reversible watermarking for relational databases

As the size of database grows, the possibility of database corruption also increases. One such example is of temporal databases in which deletion never occurs except in case of vacuuming. A strong security mechanism is needed to find any database modification. In case of any tampering, tampered data should be identified and recovery of original data from the tampered one is also essential. In this work, a new watermarking scheme for database authentication and forensic analysis is developed. The proposed system uses a set of watermark bits to make a validation and recovery mechanism for database authentication. In order to measure the robustness of this approach, online available yahoo financial data is watermarked through this approach and simulation of insertion, modification and deletion attacks are performed. Normalised correlation (NC) and mean square error (MSE) are used for measuring the performance of this approach. Extensive analysis shows that the proposed method is robust against various forms of database attacks, including insertion, deletion and modification. In future, in order to identify the best possible locations for embedding the watermark, optimisation algorithms can be used. Also methods may be developed for enhancing the embedding capacity of the watermark.

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review.

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

Short tandem repeat profiling for the authentication of cancer stem-like cells.

Colorectal and glioblastoma cancer stem‐like cells (CSCs) are essential for translational research. Cell line authentication by short tandem repeat (STR) profiling ensures reproducibility of results in oncology research. This technique enables to identify mislabeling or cross‐contamination of cell lines. In our study, we provide a reference dataset for a panel of colorectal and glioblastoma CSCs that allows authentication. Each cell line was entered into the cell Line Integrated Molecular Authentication database 2.1 to be compared to the STR profiles of 4485 tumor cell lines. This article also provides clinical data of patients from whom CSCs arose and data on the parent tumor stage and mutations. STR profiles and information of our CSCs are also available in the Cellosaurus database (ExPASy) as identified by unique research resource identifier codes.

Development and application of perfect SSR markers in cotton

BackgroundThis study aimed to develop a set of perfect simple sequence repeat (SSR) markers with a single copy in the cotton genome, to construct a DNA fingerprint database suitable for authentication of cotton cultivars. We optimized the polymerase chain reaction (PCR) system for multi-platform compatibility and improving detection efficiency. Based on the reference genome of upland cotton and 10× resequencing data of 48 basic cotton germplasm lines, single-copy polymorphic SSR sites were identified and developed as diploidization SSR markers. The SSR markers were detected by denaturing polyacrylamide gel electrophoresis (PAGE) for initial screening, then fluorescence capillary electrophoresis for secondary screening. The final perfect SSR markers were evaluated and verified using 210 lines from different sources among Chinese cotton regional trials.ResultsUsing bioinformatics techniques, 1 246 SSR markers were designed from 26 626 single-copy SSR loci. Adopting a stepwise (primary and secondary) screening strategy, a set of 60 perfect SSR markers was selected with high amplification efficiency and stability, easy interpretation of peak type, multiple allelic variations, high polymorphism information content (PIC) value, uniform chromosome distribution, and single-copy characteristics. A multiplex PCR system was established with ten SSR markers using capillary electrophoresis detection.ConclusionsA set of perfect SSR markers of cotton was developed and a high-throughput SSR marker detection system was established. This study lays a foundation for large-scale and standardized construction of a cotton DNA fingerprint database for authentication of cotton varieties.

DNA barcoding, phytochemical screening and antimicrobial activity of Rhododendron arboreum, a high altitudinal medicinal plant from Nepal

DNA barcoding is been proposed as a powerful taxonomic tool for species identification genetically. The Consortium for the Barcode of Life (CBOL) Plant Working Group has recommended the combination of rbcL & matK as the core plant barcode. Rhododendron of Nepal shows genetic diversity due to which 31 different species were reported till date, found in Nepal based on morphological character. We collected different samples from the central and western parts of Nepal (Province 3 & 5). For the phytochemical study, cold extraction was performed using methanol as solvents. The crude extracts were tested for the presence of phytochemicals such as alkaloids, saponins, glycosides, tannins, flavonoids, and coumarins. The same extracts were used for antimicrobial assay five different pathogenic micro-organisms. MIC was done for calculating the minimum effective concentration of the extract. Then TLC was done to separate the compound. The methanol extract was analyzed for core secondary metabolites by HPLC-QTOF and found more than 20 medicinal important compounds. DPPH assay was also done with crude extracts for identifying antioxidant potency of the extracts. Among samples compared, Gulmi and Palpa showed the best result for antioxidant assay with minimum IC50. Similarly, phenolic content was also checked and samples from Palpa showed high phenolic content. The rbcL and MatK gene loci show the 99.98% similarly for the R.arboreum in NCBI database and were submitted to the BOLD database for the final authentication and get success for the first time to get published DNA barcode of Nepalese plants.

Authentication Database Leakage Detection

Authentication is the first step for any important applications. Password verification is widely employed in the Internet. Some advanced mechanisms including bio features, such as fingerprints or retina. However, digital password authentication credential is commonly used in the public. In other words, traditional password verification is still an important authentication credential mechanism for today’s online services. Password database may be compromised. Once if the database is stolen, it is dangerous and critical for any services. Consequently, password database leakage becomes an important issue. Detection and countermeasure are essential for such a disaster. Our research proposing a scheme based on a authentication database storage mapping method to detect possible leakage of authentication database. By multiple mapping mechanism, if the authentication database is stolen, an attacker could not know any user’s correct password. This method can reduce additional space of storing passwords with ability to detect security event for stolen authentication databases.

Multimodal Image Fusion in Biometric Authentication

During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication, resulted in a reduction in both the spatial and temporal complexity of the process. A novel technique for safeguarding fingerprint privacy has been developed to contribute to the study. This system integrates the unique fingerprints of the thumb, index finger, and middle finger into a single new template. It was suggested that the Fixed-Size Template (FEFST) technique may be used might develop a novel strategy for the extraction of fingerprint features. From each of the fingerprints, the minute locations of the ridge end and ridge bifurcations as well as their orientations relative to the reference points were retrieved. The primary template was derived from the fingerprint that included the greatest number of ridge ends. For the purpose of generating the combined minutiae template, the templates of the other two fingerprints were incorporated into this template. The merged minutiae template that was developed was then saved in a database so that registration could take place. During the authentication process, the system received the three query fingerprints, and those fingerprints were compared to the previously saved template.

Freehand Sketch-Based Authenticated Security System using Convolutional Neural Network

An Authenticated Security System is a highly desired feature. In this paper, a FreeHand Sketch-based Authentication Security strategy is proposed for authentication purposes by allowing a user to choose one label from a collection of different labels and asking him to sketch the corresponding image for the selected label for registration to avoid mischievous registration and the sketched image gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained Convolutional Neural Network(CNN) data model to generate the necessary image label. The produced image label will compare with selected image label. If both are same then the details will store in the system database. The user gets login with his/her authorized details with sketch based image password. The image password gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained CNN model to produce the image name. The produced image name will compare with the system database for authentication. The methodology is tested with some sample input image passwords and the performance calculation is carried out using metrics like Recall and Precision. The proposed work exhibits the accuracy of approximately 85% by ensuring the authentication for the user security.