No abstract

No abstract

Security is a critical part of information systems and must be integrated into every aspect of the system. It requires a lot of expertise to design and implement secure systems due to the broad coverage of security issues and threats. A good system design is based on sound software engineering principles which leverages proven best practices in the form of standard guidelines and design patterns. A design pattern represents a reusable solution to a recurring problem in a specific context. The current security design pattern landscape contains several patterns, pattern catalogs and pattern classification schemes. To apply appropriate patterns for a specific problem context, a deeper understanding of this domain is essential. A survey of patterns and their classification schemes will aid in understanding pattern coverage and identifying gaps. In this paper, the authors have presented a detailed exploratory study of the security design pattern landscape. Based on their study, the authors have identified shortcomings and presented future research directions.

Innovation in latest technologies have provided means to gather data using various ways. Almost all the domains generate, store, and analyze the data for the improvement of the services they provide. The healthcare industry too generates a significant data which is used for improving public healthcare. While dealing with health data, it is necessary to follow appropriate data ethics as health data is considered the most sensitive and it needs to be properly collected, stored, processed and shared with different domains. This research paper discusses about the various data ethics to be followed to handle individual's health data, suggests a framework to deal with this data and a use case is suggested to understand the data ethics tenets.

Design patterns are useful Software Engineering tools that enable the reuse of expert solutions to recurring problems. There are a large number of patterns, spread in multiple catalogs and in heterogeneous formats. Selecting and applying the right design pattern requires an in-depth understanding of patterns and their classification. The solution architects must either rely on the advice of experts or laboriously go through the available literature to find the relevant patterns. Pattern applicability will improve if the entire pattern knowledge is available in one place and in a standard format. If the pattern data is augmented with additional knowledge to guide the architect on choosing the right patterns for a particular requirement, it will be immensely useful and productive. The objective of the knowledge discovery process on the design pattern landscape is to extract useful relations and groups of patterns to enable users to select and apply patterns effectively. The present work discusses a model for analyzing existing pattern data, extracting knowledge thereof, and representing this knowledge in a format to enable pattern search and its application.