Obtain confidentiality or/and authenticity in Big Data by ID-based generalized signcryption

Recently, the Big Data paradigm has received considerable attention since it gives a great opportunity to mine knowledge from massive amounts of data. However, the new mined knowledge will be useless if data is fake, or sometimes the massive amounts of data cannot be collected due to the worry on the abuse of data. This situation asks for new security solutions. On the other hand, the biggest feature of Big Data is "massive", which requires that any security solution for Big Data should be "efficient". In this paper, we propose a new identity-based generalized signcryption scheme to solve the above problems. In particular, it has the following two properties to fit the efficiency requirement. (1) It can work as an encryption scheme, a signature scheme or a signcryption scheme as per need. (2) It does not have the heavy burden on the complicated certificate management as the traditional cryptographic schemes. Furthermore, our proposed scheme can be proven-secure in the standard model. © 2014 Elsevier Inc. All rights reserved

Big data analytics: balancing individuals’ privacy rights andbusiness interests

This research thesis analyses and discusses the importance of having a legal framework that can control and manage the use of data during the Big Data analysis process. The thesis firstly examines the data analytics technologies, such as Hadoop Distributed File System (HDFS) and the technologies that are used to protect data during the analytics process. Then there is an examination of the legal principles that are part of the new General Data Protection Regulation (GDPR), and the other laws that are in place in order to manage the new era of Big Data analytics. Both the legal principles Chapter and data analytics Chapter are part of the literature review. The IT section of the literature review begins with an analysis of the data analytics technologies, such as HDFS and Map-Reduce. The second part consists of the technologies to protect privacy, especially with respect to protection during the data generation phase. Furthermore, there is a discussion on whether these current technologies are good enough to provide protection for personal data in the Big Data age. The legal section of the literature review starts by discussing some risk mitigation schemes that can be used to help individuals protect their data. This is followed by an analysis of consent issues in the Big Data era and later by an examination of the important legal principles that can help to control the Big Data process and ultimately protect individuals’ personal data. The motivation for carrying out this research was to examine how Big Data could have an effect on ordinary individuals, specifically with respect to how their data and privacy could be infringed during the data analytics process. This was done by bringing together the Big Data worlds from the legal and technological perspective. Also, by hearing the thoughts and views of those individuals who could be affected, and hearing from the experts who could shine a light on the realities in the Big Data era. The research includes the analysis and results of three surveys, constituting over 100 respondents, who expressed their views on a number of issues, including their fears about privacy online. This included a survey of mainly closed questions for students at Canterbury Christ Church University, a survey monkey survey for students at University College Cork, in Ireland and finally a survey for students in Sri Lanka. Questions were posed to some experts in areas of IT law and Big Data analytics and security. The results of these interviews were analysed and discussed, producing much debate with respect to what can be done to manage and protect citizens’ personal data privacy in the age of Big Data analytics. The software packages Statistical Package for the Social Sciences (SPSS) and Minitab were used to analyse the results of the surveys, while Qualitative Data Analysis Miner (QDA miner) software was used to analyse the results of the interviews