A steganography is an art of hiding confidential data into digital media such as image, audio, video etc. Texture synthesis uses the concept of the patch which represents an image block of source texture where its size is user specified. A texture synthesis process resamples a smaller texture image and provides a new image with arbitrary size and shape. Instead of using an existing cover image to hide messages, the algorithm conceals the source texture image and embeds secret messages using the process of texture synthesis. This allows extracting the hidden messages and source texture from a stego synthetic texture. This offers the advantages like, First, it provides the embedding capacity that is proportional to the size of the stego texture image. Second, the reversible capability inherited from this includes functionality, which allows recovery of the source texture. And third, there will be no image distortion since the size of the new texture image is user specified.

Privacy is the key factor to handle personal and sensitive data, which in large chunks, is stored by database management systems (DBMS). It provides tools and mechanisms to access and analyze data within it. Privacy preservation converts original data into some unknown form, thus protecting personal and sensitive information. Different access control mechanisms such as discretionary access control, mandatory access control is used in DBMS. However, they hardly consider purpose and role-based access control in DBMS, which incorporates policy specification and enforcement. The role based access control (RBAC) regulates the access to resources based on the roles of individual users. Purpose based access control (PuBAC) regulates the access to resources based on purpose for which data can be accessed. It regulates execution of queries based on purpose. The PuRBAC system uses the policies of both, i.e. PuBAC and RBAC, to enforce within RDBMS.

Association Rule mining (ARM) is well studied and famous optimization problem which finds useful rules from given transactional databases. Many algorithms already proposed in literature which shows their efficiency when dealing with different sizes of datasets. Unfortunately, their efficiency is not enough for handling large scale datasets. In this case, Bees swarm optimization algorithm for association rule mining is more efficient. These kinds of problems need more powerful processors and are time expensive. For such issues solution can be provided by graphics processing units (GPUs) and are massively multithreaded processors. In this case GPUs can be used to increase speed of the computation. Bees swarm optimization algorithm for association rule mining can be designed using GPUs in multithreaded environment which will efficient for given datasets.