M. Hemalatha

An efficient secure code approach based on indexed table quasi group encryption with Hadamard and Number Theoretic Transformation for software protection

Software security has become one of the active areas of research due to various cyber threats and attacks that can be very dangerous. A majority of these software threats directly affects the security aspects such as confidentiality, integrity and accessibility. These software threats such as reverse engineering, tampering, etc., exploit the authenticated data of the software. Various techniques have been developed to deal with the software threats and attacks. But, most of the existing software protection techniques do not provide reliable security against various attacks. Recently, Cryptographic approaches are found to be very effective in handling the software threats and attacks. Code encryption has received much attention in the field of software security. This paper proposes a novel software protection code encryption scheme based on the index table. This approach uses a novel and efficient encryption technique called quasigroup encryption for encryption the indexed table. It provides least resemblance of the original data when encrypted. Quasigroups effectively produce an astronomical number of keys which confuses the hackers in identifying the original data. But, quasi group encryption is not efficient in diffusing the statistics of the plain text. Hence, this approach uses chained Hadamard transforms and Number Theoretic Transforms to introduce diffusion along with the quasigroup transformation. The experimental results evaluate the performance of the proposed encryption approach based on the time cost and space cost and it is observed that the proposed approach provides significant results.

A new deferred cleansing technique for effective warehousing of RFID

RFID is a recent technology that has been widely used in education, supply chain management, military, airline, library, security, healthcare, animal farms and other areas. RFID tags store unique identification information of objects and communicate with the reader. The data observed by the reader are very dirty and unreliable. Our major aim was to design an RFID based application that would provide efficient means to perform essential information management for health care domain and to use the error free data for high-end applications. Small mistakes in healthcare could cause huge loss of life and incur massive financial losses. Data quality has become increasingly important to many organizations specifically for healthcare. So we have simulated the RFID in healthcare and next as a challenge took the dirty RFID data to perform cleaning in an effective manner using our newly proposed algorithm. Our approach is a hybrid approach of middleware and deferred because it is not always possible to remove all anomalies and redundancies in middleware. The processing of other anomalies is deferred until the query time and cleaned by business rules. Experimental results show that the proposed approach performs the cleaning in an effective manner compared to the existing approaches.

CBADE: Hybrid Approach for Duplication Detection and Elimination

Radio Frequency Identification (RFID) is an automatic data capturing technology and the dirty data stream generated by the RFID readers is one of the main factor limit the widespread adoption of RFID technology. In order to provide reliable data to RFID application, it is necessary to clean the collected data before they are subjected to warehousing. In this paper we are going to construct the elegant hospital environment using RFID and developed the cellular based approach algorithm to clean the duplication anomaly. First middleware approach is applied to deal with low complex anomalies and in next stage deferred approach is followed to deal with high complex anomalies based on business context. Simulation shows our cleansing approach for duplication removal deals with RFID data more accurately and efficiently. Thus we can bring down the health care costs, optimize business processes, streamline patient identification processes and improve patient safety.

Efficient method for feature extraction on video preocessing

A feature is defined as a role of one or more measurements, the values of some irrefutable assets of an object, computed so that it quantifies some significant characteristics of an object. Here in this paper we can have presented a clearer view of how feature extraction takes place on the basis of color, texture, and shape in video processing. The variation that occurs during feature extraction is also shown. The Iteration in which the objects are identified from a specified location is also considered. The proposed algorithm shows that the objects are identified correctly in a lower number of iterations itself when compared to the traditional algorithms.

Anomaly Detection and Elimination Algorithm for RFID Data in Healthcare

The RFID technology has penetrated into all the sectors like supply chain automation, asset tracking, medical/Health Care applications, people tracking, Manufacturing, Retail, Warehouses, Livestock Timing and the healthcare sector due to its increased functionality, low cost, high reliability and easy-to-use capabilities. RFID system produces data that are unreliable, low-level, and rarely able to be used directly by applications. This paper discusses the existing physical, middleware and deferred approaches to deal with anomalies. Each approach has its own drawbacks. To clean the anomaly false positive in an effective manner we have chosen the integrated approach of middleware and deferred. The premise taken is based on cellular for detecting out of the range readings .The RFID readers have Omni-directional antenna and hence there are possibilities for the adjacent regions to over lap with each other. Algorithm proposed in this paper do not deal with any physical device , but rather integrate middleware and deferred to construct RFID hybrid system that lighten issues associated with using RFID data through adaptive cleaning technique. The resultant data is cleaned data and it can be used for any high end applications. Simulation shows our approach deals with RFID data more efficiently and accurately.