In today's digital era, it is a very common practice for organizations to collect data from individual users. The collected data is then stored in multiple databases which contain personally identifiable information (PII). This may lead to a major source of privacy risk for the database. Various privacy preservation techniques have been proposed such as perturbation, anonymization and cryptographic. In this study, five anonymization techniques are compared using the same dataset. In addition to that, this study reviews the strengths and weaknesses of the different technique. In the evaluation of efficiency, suppression is found as the most efficient while swapping is in the last place. It is also revealed that swapping is the most resource-consuming technique while suppressing being less resource consuming.

Generally, E-nose mimics human olfactory sense to detect and distinguish an odor or gasses or volatile organic compound from a few objects such as food, chemicals, explosive etc. Thus, E-nose can be used to measure gas emitted from food due to its ability to measure gas and odor. Principally, the E-nose operates by using a number of sensors to response to the odorant molecules (aroma). Each sensor will respond to their specific gas respectively. These sensors are a major part of the electronic nose to detect gas or odor contained in a volatile component. Information about the gas detected by sensors will be recorded and transmitted to the signal processing unit to perform the analysis of volatile organic compound (VOC) pattern and stored in the database classification, in order to determine the type of odor. Classification is a way to distinguish a mixture odor/aroma obtained from gas sensors in an electric signal form. In this paper, we discussed briefly about electronic nose, it’s principle of work and classification method and in order to classify food freshness.

The concerns in healthcare services is needed to deal with privacy and data security risks in handling electronic healthcare records (EHR). With the limitation of the existing literature dealing with number of factors that influence information privacy concerns in EHR, the main objective of this study is to review recent studies on information privacy concerns in EHR and then also to categorize the related factors. A total of 199 articles were extracted using a predefined search string. A quality criterion was applied on this set of articles, a total of 18 articles were determined for further analysis. Nine factors were found to influence information privacy concerns. However, two factors, namely information dissemination and computer literacy were commonly revealed as factors attributing to information privacy concerns in the healthcare domain. The unit of analysis was further examined from two perspectives; healthcare practitioners and patients.

Advanced Metering Infrastructure (AMI) is a component of electrical networks that combines the energy and telecommunication infrastructure to collect, measure and analyze consumer energy consumptions. One of the main elements of AMI is a smart meter that used to manage electricity generation and distribution to end-user. The rapid implementation of AMI raises the need to deliver better maintenance performance and monitoring more efficiently while keeping consumers informed on their consumption habits. The convergence from analog to digital has made AMI tend to inherit the current vulnerabilities of digital devices that prone to cyber-attack, where attackers can manipulate the consumer energy consumption for their benefit. A huge amount of data generated in AMI allows attackers to manipulate the consumer energy consumption to their benefit once they manage to hack into the AMI environment. Anomalies detection is a technique can be used to identify any rare event such as data manipulation that happens in AMI based on the data collected from the smart meter. The purpose of this study is to review existing studies on anomalies techniques used to detect data manipulation in AMI and smart grid systems. Furthermore, several measurement methods and approaches used by existing studies will be addressed.

This study aims to investigate healthcare practitioner behaviour in adopting Health Information Systems which could affect patients’ safety and quality of health. A qualitative study was conducted based on a semi-structured interview protocol on 31 medical doctors in three Malaysian government hospitals implementing the Total Hospital Information Systems. The period of study was between March and May 2015. A thematic qualitative analysis was performed on the resultant data to categorize them into relevant themes. Four themes emerged as healthcare practitioners’ behaviours that influence the unsafe use of Hospital Information Systems. The themes include (1) carelessness, (2) workarounds, (3) noncompliance to procedure, and (4) copy and paste habit. By addressing these behaviours, the hospital management could further improve patient safety and the quality of patient care.