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There were 2,216 data breaches and more than 53,000 cybersecurity incidents reported in 65 countries in the 12 months ending in March 2018. The year of 2018 was certainly a busy one for the information security industry. Threats and breaches were aplenty and whilst trends like DDoS (Distributed Denial of Service) and ransomware played a notably less significant role than in 2017, phishing attacks hit companies hard and business email compromise skyrocketed over the course of the year. This year also saw the growth of a few new threats that seemed to catch many by surprise. As 2018 draws to a close, the industry is now preparing for the trends, threats and challenges that 2019 might have in store for businesses and consumers. This article has summarized the predictions by industry experts about threats in cybersecurity. Many industry experts have predicted various security threats, researcher tried to address many of them. The main focus area of this research work is to highlight potential threats in upcoming years. [ABSTRACT FROM AUTHOR]

In this digital information era, the data is an important asset of the organization so the security of the data is a vital role in the industry. De-identification is most commonly undertaken to protect the privacy of individuals. It aims to allow data to be used by others without the possibility of individuals being identified. Several U.S laws, regulations and policies specify that data should be de-identified prior to sharing [4]. De-identification is not a single technique, but a collection of approaches, algorithms, and tools that can be applied to different kinds of data with differing levels of effectiveness. This research paper provides an overview of de-identification issues and terminology. It summarizes significant publications to date involving de-identification and re-identification. Paper also covers importance and utility of data deidentification, De-Identification Data Flow Model and relation between De-Identification and Masking. Authors do not make recommendations regarding the appropriateness of de-identification or specific de-identification algorithms. [ABSTRACT FROM AUTHOR]