Web-Based Education for All: A Tool for Development Adaptive Courseware.

https://www.bmj.com/content/bmj/295/6610/1418.1.full.pdf

Data Protection Act.

Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall cost

Secure and Trustable Electronic Medical Records Sharing using Blockchain

Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and disclosed for secondary purposes. It has also been proposed as an appropriate model for protecting health data. In this paper we review the current literature on differential privacy and highlight important general limitations to the model and the proposed mechanisms. We then examine some practical challenges to the application of differential privacy to health data. The most severe limitation is the theoretical nature of the privacy parameter epsilon. It has implications on our ability to quantify the level of anonymization that would be guaranteed to patients, as well as assessing responsibilities when a privacy breach occurs. The review concludes by identifying the areas that researchers and practitioners need to address to increase the adoption of differential privacy for health data.

http://www.tdp.cat/issues11/tdp.a129a13.pdf

Practicing Differential Privacy in Health Care: A Review

Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and disclosed for secondary purposes. It has also been proposed as an appropriate model for health data. In this paper we review the current literature on differential privacy and highlight important general limitations to the model and the proposed mechanisms. We then examine some practical challenges to the application of differential privacy to health data. The review concludes by identifying areas that researchers and practitioners in this area need to address to increase the adoption of differential privacy for health data.

http://www.mathcs.emory.edu/~lxiong/pais12/pdf/PAIS12_Paper2.pdf

The application of differential privacy to health data

Anonymization is a practical approach to protect privacy in data. The major objective of privacy preserving data publishing is to protect private information in data whereas data is still useful for some intended applications, such as building classification models. In this paper, we argue that data generalization in anonymization should be determined by the classification capability of data rather than the privacy requirement. We make use of mutual information for measuring classification capability for generalization, and propose two k-anonymity algorithms to produce anonymized tables for building accurate classification models. The algorithms generalize attributes to maximize the classification capability, and then suppress values by a privacy requirement k (IACk) or distributional constraints (IACc). Experimental results show that algorithm IACk supports more accurate classification models and is faster than a benchmark utility-aware data anonymization algorithm.

Information based data anonymization for classification utility

A hybrid approach to prevent composition attacks for independent data releases

Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data owner. However, no method has been proposed for the anonymization of data of multiple independent data publications. A data owner publishes a dataset, which contains overlapping population with other datasets published by other independent data owners. In this paper we analyze the privacy risk in the such scenario and vulnerability of partitioned based anonymization methods. We show that no partitioned based anonymization methods can protect privacy in arbitrary data distributions, and identify a case that the privacy can be protected in the scenario. We propose a new generalization principle e-cloning to protect privacy for multiple independent data publications. We also develop an effective algorithm to achieve the e-cloning. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves good data utility.

Cloning for privacy protection in multiple independent data publications

Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. A data holder publishes a dataset, which contains overlapping population with other datasets published by other independent data holders. No existing methods are able to protect privacy in such multiple independent data publishing. In this paper we propose a new generalization principle (ρ,α)-anonymization that effectively overcomes the privacy concerns for multiple independent data publishing. We also develop an effective algorithm to achieve the (ρ,α)-anonymization. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves high quality data utility.

Data privacy against composition attack

Personal Learning Environments PLEs help the learners to take control of their learning. PLEs enable the learners to set their own leaning targets and manage their learning by communicating with others in the process of learning. As latest technological advancements have brought revolution in every field of life, so as in the PLEs. Modern PLEs are the integration of a number of latest technologies i.e. blogs, Wikis, RSS feeds, where content is shaped as per the individual needs and interests of the students. Focusing on these latest aspects of the PLEs, University of South Australia initiated a three year new learning platform project in 2010, called LearnOnline, which will replace the University's current online teaching environment UniSAnet. LearnOnline was launched with a vision to foster richer learning through promoting students' active involvement in their courses and involving the students in a deeper learning experience. LearnOnline is built on modular approach and consists of different components i.e. ePortfolio, Course Outline, Lecture Recording, Copyright Monitoring, Student Email, Assessment and Feedback, Virtual Classroom, Course and Teacher Evaluation. Each component is developed separately and is fully independent. This methodology is helping the incremental implementation of the LearnOnline. As soon as a component is completed, after testing, it becomes the part of LearnOnline. In this paper, the author explains the features and workings of LearnOnline in detail and also evaluates its design methodologies.

Muzammil M. Baig

Papers

Information based data anonymization for classification utility

A hybrid approach to prevent composition attacks for independent data releases

Cloning for privacy protection in multiple independent data publications

Data privacy against composition attack

LearnOnline: Personal Learning Environment Implementation in University of South Australia