No abstract

No abstract

Information and communications technology (ICT) has expanded commercial potential beyond comprehension and has merged completely with organizations. In addition to its many advantages, ICT has disadvantages, such as cybersecurity risks, vulnerabilities, and a lack of adequate administrative access control that cybercriminals might take advantage of. Organizations are becoming more dependent on information security as a result of potential hazards brought on by advances in information technology, each of which has a different critical level based on its likelihood of happening and potential consequences. Methodologies for evaluating information security threats can be either quantitative or qualitative, depending on the Information outcome of their assessment. It is clear that both of the aforementioned choices have a number of fundamental flaws. In order to overcome them, this research paper focused on developing an Integrated Information Security Risk Assessment (IISRA) Framework that would be both more accurate and adaptable because existing approaches are frequently inappropriate and ineffectual due to the ongoing appearance of new sources of risks.

Most of our daily activities are now routinely recorded and analysed by a variety of governmental and commercial organizations for the purpose of security and business related applications. From telephone calls to credit card purchases, from internet surfing to medical prescription refills, we generate data with almost every action we take. These data sets need to be analyzed for identifying patterns which can be used to predict future behaviour. However, data owners may not be willing to share the real values of their data due to privacy reason. Hence, some amount of privacy preservation needs to be done on data before it is released. Privacy preserving data mining (PPDM) tends to transform original data, so that sensitive data are preserved. In this paper we have proposed a new method CAMDP (Combination of Additive and Multiplicative Data Perturbation) for privacy preserving in data mining.

No abstract