Preservation of Data Integrity in Public Cloud Using Enhanced Vigenere Cipher Based Obfuscation

Today’s internet world is moves to cloud computing to maintain their public data privately in a secure way. In cloud scenario, many security principles are implemented to maintain the secure transmission of data over the internet. And still, the main concern is about maintaining the integrity of our own data in public cloud. Mostly, research works concentrates on cryptographic techniques for secure sharing of data but there is no such mentioned works are available for data integrity. In this paper, a data masking technique called obfuscation is implemented which is used to protect the data from unwanted modification by data breaching attacks. In this work, enhanced Vigenere encryption is used to perform obfuscation that maintains the privacy of the user’s data. Enhanced Vigenere encryption algorithm combined with intelligent rules to maintain the dissimilarity between the data masking for perform encryption with different set of rules. This work mainly concentrates on data privacy with reduced time complexity for encryption and decryption.

Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images

In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.

Decision Tree-Based Data Stratification Method for the Minimization of the Masking Effect in Adverse Drug Reaction Signal Detection

Data masking is an inborn defect of measures of disproportionality in adverse drug reactions signal detection. Some improved methods which used gender and age for data stratification only considered the patient-related confounding factors, ignoring the drug-related influencing factors. Due to a large number of reports and the high proportion of antibiotics in the Chinese spontaneous reporting database, this paper proposes a decision tree-stratification method for the minimization of the masking effect by integrating the relevant factors of patients and drugs. The adverse drug reaction monitoring reports of Jiangsu Province in China from 2011 to 2018 were selected for this study. First, the age division interval was determined based on the statistical analysis of antibiotic-related data. Secondly, correlation analysis was conducted based on the patient’s gender and age respectively with the drug category attributes. Thirdly, the decision tree based on age and gender was constructed by the J48 algorithm, which was used to determine if drugs belonged to antibiotics as a classification label. Fourthly, some performance evaluation indicators were constructed based on the data of drug package inserts as a standard signal library: recall, precision, and F (the arithmetic harmonic mean of recall and precision). Finally, four experiments were carried out by means of the proportional reporting ratio method: non-stratification (total data), gender-stratification, age-stratification and decision tree-stratification, and the performance of the signal detection results was compared. The experimental results showed that the decision tree-stratification was superior to the other three methods. Therefore, the data-masking effect can be further minimized by comprehensively considering the patient and drug-related confounding factors.

An efficient data masking method for encrypted 3D mesh model

The industrial 3D mesh model (3DMM) plays a significant part in engineering and computer aided designing field. Thus, protecting copyright of 3DMM is one of the major research problems that require significant attention. Further, the industries started outsourcing its 3DMM to cloud computing (CC) environment. For preserving privacy, the 3DMM are encrypted and stored on cloud computing environment. Thus, building efficient data masking of encrypted 3DMM is considered to be efficient solution for masking information of 3DMM. First, using the secret key, the original 3DMM is encrypted. Second without procuring any prior information of original 3DMM it is conceivable mask information on encrypted 3D mesh models. Third, the original 3DMM are reconstructed by extracting masked information. The existing masking methods are not efficient in providing high information masking capacity in reversible manner and are not robust. For overcoming research issues, this work models an efficient data masking (EDM) method that is reversible nature. Experiment outcome shows the EDM for 3DMM attain better performance in terms of peak signal-to-noise ratio (PSNR) and root mean squared error (RMSE) over existing data masking methods. Thus, the EDM model brings good tradeoffs between achieving high data masking capacity with good reconstruction quality of 3DMM.

A Secured Mobile Money Transaction Using Data Masking and Enhanced Base64 Algorithm

<em>There is always a need to transfer money from one user to another for either payment of services or settlement of business transactions and so on. Research has shown that traditional money transaction systems are prone to attacks through falsified deposit slips and drafts, theft of debit cards, forgery of signatures, use of false cheques and so on. Electronic money transaction is a payment performed from an electronic device which enables users to have access to their money anywhere and at any time with the aid of a network but not adequately secured. This application offers a platform independent of securing and transferring money using data masking and an enhanced base64 algorithm from one account to another. The study improves on existing money transfer and transaction systems by achieving a secured mobile money transaction system with masked and encrypted financial details both on the mobile application and also on the short message service application (Text Message Notification) sent to user’s platform which makes it difficult for third party to intercept and understand. </em>

An efficient data masking for securing medical data using DNA encoding and chaotic system

Data security is utmost important for ubiquitous computing of medical/diagnostic data or images. Along with must consider preserving privacy of patients. Recently, deoxyribose nucleic acid (DNA) sequences and chaotic sequence are jointly used for building efficient data masking model. However, the state-of-art model are not robust against noise and cropping attack (CA). Since in existing model most digits of each pixel are not altered. This work present efficient data masking (EDM) method using chaos and DNA based encryption method for securing health care data. For overcoming research challenges effective bit scrambling method is required. Firstly, this work present an efficient bit scrambling using logistic sine map and pseudorandom sequence using chaotic system. Then, DNA substitution is performed among them to resist against differential attack (DA), statistical attack (SA) and CA. Experiment are conducted on standard considering diverse images. The outcome achieved shows proposed model efficient when compared to existing models.