Protecting Patient Privacy: Strategies for Regulating Electronic Health Records Exchange

The report offers policymakers 10 recommendations to protect patient privacy as New York state develops a centralized system for sharing electronic medical records. Those recommendations include:Require that the electronic systems employed by HIEs have the capability to sort and segregate medical information in order to comply with guaranteed privacy protections of New York and federal law. Presently, they do not.Offer patients the right to opt-out of the system altogether. Currently, people's records can be uploaded to the system without their consent.Require that patient consent forms offer clear information-sharing options. The forms should give patients three options: to opt-in and allow providers access to their electronic medical records, to opt-out except in the event of a medical emergency, or to opt-out altogether.Prohibit and sanction the misuse of medical information. New York must protect patients from potential bad actors--that small minority of providers who may abuse information out of fear, prejudice or malice.Prohibit the health information-sharing networks from selling data. The State Legislature should pass legislation prohibiting the networks from selling patients' private health information

Usability and Trust in Information Systems

The need for people to protect themselves and their assets is as old as humankind. People's physical safety and their possessions have always been at risk from deliberate attack or accidental damage. The advance of information technology means that many individuals, as well as corporations, have an additional range of physical (equipment) and electronic (data) assets that are at risk. Furthermore, the increased number and types of interactions in cyberspace has enabled new forms of attack on people and their possessions. Consider grooming of minors in chat-rooms, or Nigerian email cons: minors were targeted by paedophiles before the creation of chat-rooms, and Nigerian criminals sent the same letters by physical mail or fax before there was email. But the technology has decreased the cost of many types of attacks, or the degree of risk for the attackers. At the same time, cyberspace is still new to many people, which means they do not understand risks, or recognise the signs of an attack, as readily as they might in the physical world. The IT industry has developed a plethora of security mechanisms, which could be used to mitigate risks or make attacks significantly more difficult. Currently, many people are either not aware of these mechanisms, or are unable or unwilling or to use them. Security experts have taken to portraying people as "the weakest link" in their efforts to deploy effective security [e.g. Schneier, 2000]. However, recent research has revealed at least some of the problem may be that security mechanisms are hard to use, or be ineffective. The review summarises current research on the usability of security mechanisms, and discusses options for increasing their usability and effectiveness

Usable Security: Why Do We Need It? How Do We Get It?

Security experts frequently refer to people as “the weakest link in the chain” of system security. Famed hacker Kevin Mitnick revealed that he hardly ever cracked a password, because it “was easier to dupe people into revealing it” by employing a range of social engineering techniques. Often, such failures are attributed to users’ carelessness and ignorance. However, more enlightened researchers have pointed out that current security tools are simply too complex for many users, and they have made efforts to improve user interfaces to security tools. In this chapter, we aim to broaden the current perspective, focusing on the usability of security tools (or products) and the process of designing secure systems for the real-world context (the panorama) in which they have to operate. Here we demonstrate how current human factors knowledge and user-centered design principles can help security designers produce security solutions that are effective in practice

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Memories for Life: A Review of the Science and Technology

This paper discusses scientific, social and technological aspects of memory. Recent developments in our understanding of memory processes and mechanisms, and their digital implementation, have placed the encoding, storage, management and retrieval of information at the forefront of several fields of research. At the same time, the divisions between the biological, physical and the digital worlds seem to be dissolving. Hence opportunities for interdisciplinary research into memory are being created, between the life sciences, social sciences and physical sciences. Such research may benefit from immediate application into information management technology as a testbed. The paper describes one initiative, Memories for Life, as a potential common problem space for the various interested disciplines

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future