The Challenges of New Information Technology on Security, Privacy and Ethics

The rapid rate of growth and change in Information technology continues to be a challenge for those in the information sector. New technologies such as the Internet of Things (IoT) and wearables, big data analytics, and artificial intelligence (AI) are developing so rapidly that information security and privacy professionals are struggling to keep up. Government and industry call for more cybersecurity professionals and the news media make it clear that the number of cybersecurity breaches and incidents continues to rise. This short article exams some of the challenges with the new technologies and how they are vulnerable to exploitation. In order to keep pace, information security education, ethics, governance and privacy controls must adapt. Unfortunately, as history shows us, they are slow to evolve, much slower than the technologies we hope to secure. The 2020s will usher in vast advancements in technology. More attention needs to be given to anticipating the vulnerabilities associated with that technology and the strategies for mitigating them

Framework to Avoid Similarity Attack in Big Streaming Dat

The existing methods for privacy preservation are available in variety of fields like social media, stock market, sentiment analysis, electronic health applications. The electronic health dynamic stream data is available in large quantity. Such large volume stream data is processed using delay free anonymization framework. Scalable privacy preserving techniques are required to satisfy the needs of processing large dynamic stream data. In this paper privacy preserving technique which can avoid similarity attack in big streaming data is proposed in distributed environment. It can process the data in parallel to reduce the anonymization delay.  In this paper the replacement technique is used for avoiding similarity attack. Late validation technique is used to reduce information loss. The application of this method is in medical diagnosis, e-health applications, health data processing at third party

Big Data LifeCycle: Threats and Security Model

Big data is an emerging term referring to the process of managing huge amount of data from different sources, such as, DBMS, log files, postings of social media, and sensor data. Big data (text, number, images... etc.) could be divided into different forms: structured, semi-structured, and unstructured. Big data could be further described by some attributes like velocity, volume, variety, value, and complexity. The emerging big data technologies also raise many security concerns and challenges. In this paper, we present big data lifecycle framework. The lifecycle includes four phases, i.e., data collection, data storage, data analytics, and knowledge creation. We briefly introduce each phase. We further summarize the security threats and attacks for each phase. The big data lifecycle integrated with security threats and attacks to propose a security thread model to conduct research in big data security. Our work could be further used towards securing big data infrastructure

Service-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda

The development of analytical solutions for smart services systems relies on data. Typically, this data is distributed across various entities of the system. Cognitive learning allows to find patterns and to make predictions across these distributed data sources, yet its potential is not fully explored. Challenges that impede a cross-entity data analysis concern organizational challenges (e.g., confidentiality), algorithmic challenges (e.g., robustness) as well as technical challenges (e.g., data processing). So far, there is no comprehensive approach to build cognitive analytics solutions, if data is distributed across different entities of a smart service system. This work proposes a research agenda for the development of a service-oriented cognitive analytics framework. The analytics framework uses a centralized cognitive aggregation model to combine predictions being made by each entity of the service system. Based on this research agenda, we plan to develop and evaluate the cognitive analytics framework in future research

Could the doctrine of moral rights be used as a basis for understanding the notion of control within data protection law?

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Information & Communications Technology Law on 1 April 2018, available online at:https://doi.org/10.1080/13600834.2018.1458449. Under embargo until 1 October 2019.This article considers the notion of individual control of personal data as envisaged by the European data protection framework and makes the argument that it is a poorly-understood and under-developed concept, but that our understanding of it may be improved by way of analyses and comparisons with the doctrine of moral rights, an important constituent element of intellectual property law. The article starts by examining the concept of personal data itself, and why an enhanced level of individual control over personal data is thought to be a desirable regulatory objective. Following this, the article examines the scholarly literature pertaining to individual control of personal data, as well as a range of relevant EU policy documents. Having done so, the article argues that the notion of control is muddled and confused from both theoretical and practical perspectives. Following this, the article considers the doctrine of moral rights, and through an exploration of its theoretical and practical elements highlights why it may be of assistance in terms of enhancing our understanding of individual control in the data protection context.Peer reviewedFinal Accepted Versio

A privacy-preserving design for sharing demand-driven patient datasets over permissioned blockchains and P2P secure transfer

Sharing patient datasets curated by health institutions is critical for the advance of monitoring, surveillance and research. However, patient data is sensitive data and it can only be released under certain conditions and with previous explicit consent. Privacy preserving data sharing provides techniques to distribute datasets minimizing the risk of identification of patients. However, the sharing of datasets is typically done without considering the needs or requests of data consumers. Blockchain technologies provide an opportunity to gather those requests and share and assemble datasets using privacy-preserving methods as data and requirements on anonymity match. The architecture and design of such a solution is described, assuming an underlying permissioned blockchain network where providers such as healthcare institutions deal with consent, patient preferences and anonymity guarantees, playing a mediator role to a network of organizations

Small data as a conversation starter for learning analytics: Exam results dashboard for first-year students in higher education

Purpose - The purpose of this paper is to draw attention to the potential of “small data” to complement research in learning analytics (LA) and to share some of the insights learned from this approach. Design/methodology/approach - This study demonstrates an approach inspired by design science research, making a dashboard available to n=1,905 students in 11 study programs (used by n=887) to learn how it is being used and to gather student feedback. Findings - Students react positively to the LA dashboard, but usage and feedback differ depending on study success. Research limitations/implications - More research is needed to explore the expectations of a high-performing student with regards to LA dashboards. Originality/value - This publication demonstrates how a small data approach to LA contributes to building a better understanding