Enhancing Data Privacy and Security of Artificial Intelligence in Combating World Hunger

This paper focuses on improving data privacy and security for an AI based machine to combat world hunger. World leaders saw reducing hunger and malnutrition (SDG 2) as a key step to a more safe, fair, and sustainable communities in 2014. Ironically, hunger has not abated ever since. According to the most recent figures, the number of people who are severely underweight has increased for the second season in a row. To overcome this issue, machine learning has been implemented to flexibly learn and come up with solutions quicker than humans based on data collected throughout the world. There is however one problem with this, and it is of course, the security that is used to protect these data. Many different data security software has been released to overcome this particular issue, but they were all separated into different platforms such as Data Loss Prevention Software (DLP), Data Centric Software (DCS) and Database Security Software (DSS). Hence, this research is carried out to figure out a more efficient way to protect these valuable data stored by the Artificial Intelligence.