To realise the broad vision of pervasive computing, underpinned by the "Internet of Things" (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends towards the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy and personal safety risks that arise beyond these subsystems; that is, from the wide-scale, crossplatform openness that cloud services bring to IoT.In this paper we focus on security considerations for IoT from the perspectives of cloud tenants, end-users and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyse the current state of cloud-supported IoT to make explicit the security considerations that require further work.

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Over the past several years, acute and fatal respiratory illnesses have occurred in the habituated group of wild chimpanzees at the Mahale Mountains National Park, Tanzania. Common respiratory viruses, such as measles and influenza, have been considered possible causative agents; however, neither of these viruses had been detected. During the fatal respiratory illnesses in 2003, 2005 and 2006, regular observations on affected individuals were recorded. Cause-specific morbidity rates were 98.3, 52.4 and 33.8%, respectively. Mortality rates were 6.9, 3.2 and 4.6%; all deaths were observed in infants 2 months-2 years 9 months of age. Nine other chimpanzees have not been seen since the 2006 outbreak and are presumed dead; hence, morbidity and mortality rates for 2006 may be as high as 47.7 and 18.5%, respectively. During the 2005 and 2006 outbreaks, 12 fecal samples were collected from affected and nonaffected chimpanzees and analyzed for causative agents. Analysis of fecal samples from 2005 suggests the presence of paramyxovirus, and in 2006 a human-related metapneumovirus was detected and identified in an affected chimpanzee whose infant died during the outbreak. Our findings provide preliminary evidence that the causative agent associated with these illnesses is viral and contagious, possibly of human origin; and that, possibly more than one agent may be circulating in the population. We recommend that baseline health data be acquired and food wadge and fecal samples be obtained and bio-banked as early as possible when attempting to habituate new groups of chimpanzees or other great apes. For already habituated populations, disease prevention strategies, ongoing health monitoring programs and reports of diagnostic findings should be an integral part of managing these populations. In addition, descriptive epidemiology should be a major component of disease outbreak investigations.

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Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems -often systems-of-systems -poses accountability challenges. A key reason for this is because the details and nature of the information flows that interconnect and drive systems, which often occur across technical and organisational boundaries, tend to be invisible or opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Specifically, given the concerns regarding ever-increasing levels of automated and algorithmic decision-making, and so-called 'algorithmic systems' in general, we propose decision provenance as a concept showing much promise. Decision provenance entails using provenance methods to provide information exposing decision pipelines: chains of inputs to, the nature of, and the flow-on effects from the decisions and actions taken (at design and run-time) throughout systems. This paper introduces the concept of decision provenance, and takes an interdisciplinary (tech-legal) exploration into its potential for assisting accountability in algorithmic systems. We argue that decision provenance can help facilitate oversight, audit, compliance, risk mitigation, and user empowerment, and we also indicate the implementation considerations and areas for research necessary for realising its vision. More generally, we make the case that considerations of data flow, and systems more broadly, are important to discussions of accountability, and complement the considerable attention already given to algorithmic specifics.

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