Distributing sensitive information in the MPEG-21 multimedia framework

The MPEG-21 Multimedia Framework provides for controlled distribution of multimedia works through its Intellectual Property Management and Protection ("IPMP") Components and Rights Expression Language ("MPEG REL"). The IPMP Components provide a framework by which the components of an MPEG-21 digital item can be protected from undesired access, while MPEG REL provides a mechanism for describing the conditions under which a component of a digital item may be used and distributed. This chapter describes how the IPMP Components and MPEG REL were used to implement a series of digital rights management applications at the Cooperative Research Centre for Smart Internet Technology in Australia. While the IPMP Components and MPEG REL were initially designed to facilitate the protection of copyright, the applications also show how the technology can be adapted to the protection of private personal information and sensitive corporate information.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Privacy by information accountability for e-health systems

Privacy has become one of the main impediments for e-health in its advancement to providing better services to its consumers. Even though many security protocols are being developed to protect information from being compromised, privacy is still a major issue in healthcare where privacy protection is very important. When consumers are confident that their sensitive information is safe from being compromised, their trust in these services will be higher and would lead to better adoption of these systems. In this paper we propose a solution to the problem of patient privacy in e-health through an information accountability framework could enhance consumer trust in e-health services and would lead to the success of e-health services.

Climate change-related corporate governance information : an explanation of the difference between the supply of and demand for such information

This study investigates the gap between the climate change-related corporate governance information being disclosed by companies, and the information sought by stakeholders. To accomplish this objective we utilised previous research on stakeholder demand for information, and we conducted in-depth interviews with six corporate representatives from major Australian emission-intensive companies. Having gained and documented a rich insight into the potential factors responsible for the current gap in disclosure we find that the existence of an expectations gap; the perceived cost of providing commercially sensitive information; the limited accountability being accepted by the corporate managers; and, a lack of stakeholder pressure together contribute to the lack of disclosure. In highlighting the gap in disclosure, this study suggests strategies to reduce the gap in climate change-related corporate governance disclosures.

Information accountability and usability : are there any connections?

Availability of health information is rapidly increasing and the expansion and proliferation of health information is inevitable. The Electronic Healthcare Record, Electronic Medical Record and Personal Health Record are at the core of this trend and are required for appropriate and practicable exchange and sharing of health information. However, it is becoming increasingly recognized that it is essential to preserve patient privacy and information security when utilising sensitive information for clinical, management and administrative processes. Furthermore, the usability of emerging healthcare applications is also becoming a growing concern. This paper proposes a novel approach for integrating consideration of information accountability with a perspective from usability engineering that can be applied when developing healthcare information technology applications. A social networking user case in the healthcare information exchange will be presented in the context of our approach.

The perceived impact of interprofessional information sharing on young people about their sexual health care

This article presents the results from an analysis of data from service providers and young adults who were formerly in state care about how information about the sexual health of young people in state care is managed. In particular, the analysis focuses on the perceived impact of information sharing between professionals on young people. Twenty-two service providers from a range of professions including social work, nursing and psychology, and 19 young people aged 18–22 years who were formerly in state care participated in the study. A qualitative approach was employed in which participants were interviewed in depth and data were analysed using modified analytical induction (Bogdan & Biklen, 2007). Findings suggest that within the care system in which service provider participants worked it was standard practice that sensitive information about a young person’s sexual health would be shared across team members, even where there appeared to be no child protection issues. However, the accounts of the young people indicated that they experienced the sharing of information in this way as an invasion of their privacy. An unintended outcome of a high level of information sharing within teams is that the privacy of the young person in care is compromised in a way that is not likely to arise in the case of young people who are not in care. This may deter young people from availing themselves of the sexual health services.

A policy model for secure information flow

When a computer program requires legitimate access to confidential data, the question arises whether such a program may illegally reveal sensitive information. This paper proposes a policy model to specify what information flow is permitted in a computational system. The security definition, which is based on a general notion of information lattices, allows various representations of information to be used in the enforcement of secure information flow in deterministic or nondeterministic systems. A flexible semantics-based analysis technique is presented, which uses the input-output relational model induced by an attacker's observational power, to compute the information released by the computational system. An illustrative attacker model demonstrates the use of the technique to develop a termination-sensitive analysis. The technique allows the development of various information flow analyses, parametrised by the attacker's observational power, which can be used to enforce what declassification policies.

An efficient solution for privacy-preserving, secure remote access to sensitive data

Sharing data that contains personally identifiable or sensitive information, such as medical records, always has privacy and security implications. The issues can become rather complex when the methods of access can vary, and accurate individual data needs to be provided whilst mass data release for specific purposes (for example for medical research) also has to be catered for. Although various solutions have been proposed to address the different aspects individually, a comprehensive approach is highly desirable. This paper presents a solution for maintaining the privacy of data released en masse in a controlled manner, and for providing secure access to the original data for authorized users. The results show that the solution is provably secure and maintains privacy in a more efficient manner than previous solutions.

WikiLeaks and Information Disclosure: The Bradley Manning Case

We live in an age where information is a fluid resource, shared across the globe in real time. A continued concern is whether all infmmation, including sensitive information should be disclosed to a global audience. This issue has been brought to prominence because of groups such as WikiLeaks who believe that information protected by governments should be made available (often via whistleblowers) to the general public through sites such as theirs in order to promote open government and transparency.

This paper will focus on the issue of information disclosure and whistleblowing. In particular the paper will centre on the case of Bradley Manning and his disclosure of sensitive militmy information to WikiLeaks and the related events and subsequent effects on our global digital environment.

Environmental Information Access as a Security Risk

The United Sates was founded on the principles of freedom. Events in recent history have threatened the freedoms we as individuals enjoy. Notably, changes to government legislation and policies regarding access to environmentally sensitive information following September 11, 2001, are troubling. The government has struggled with a difficult balancing act. The public has the right of access to information, yet, information some view as sensitive or dangerous must be kept out of the hands of terrorists. This project examines and discusses the information access debate within the United States and how to best provide the public environmentally sensitive information.

The digital economy bill and the UK’s creative industries : a perspective from China

The Digital Economy Bill has been heavily criticized by consumer organizations, internet service providers and technology experts on the grounds that it will reduce the public’s ability to access politically sensitive information, impinge on citizens’ rights to privacy, threaten freedom of expression and have a chilling effect on digital innovation. Its passage in spite of these criticisms reflects, among other things, the power of the rhetoric that has been employed by its proponents. This paper examines economic arguments surrounding the digital economy debate in light of lessons from one of the world's fastest growing economies: China.

Toothless tiger, sleeping dragon : implied freedoms, internet filters and the growing culture of internet censorship in Australia

The internet by its very nature challenges an individual’s notions of propriety, moral acuity and social correctness. A tension will always exist between the censorship of obscene and sensitive information and the freedom to publish and/or access such information. Freedom of expression and communication on the internet is not a static concept: ‘Its continual regeneration is the product of particular combinations of political, legal, cultural and philosophical conditions’.

Discovering characteristics of stochastic collections of process models

Process models in organizational collections are typically modeled by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models, in the form of Petri nets, from small samples of a collection for accurate estimations of the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrary-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections.We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.