FPGA based remote code integrity verification of programs in distributed embedded systems

The explosive growth of networked embedded systems has made ubiquitous and pervasive computing a reality. However, there are still a number of new challenges to its widespread adoption that include scalability, availability, and, especially, security of software. Among the different challenges in software security, the problem of remote-code integrity verification is still waiting for efficient solutions. This paper proposes the use of reconfigurable computing to build a consistent architecture for generation of attestations (proofs) of code integrity for an executing program as well as to deliver them to the designated verification entity. Remote dynamic update of reconfigurable devices is also exploited to increase the complexity of mounting attacks in a real-word environment. The proposed solution perfectly fits embedded devices that are nowadays commonly equipped with reconfigurable hardware components that are exploited to solve different computational problems

Concurrency Attacks

Just as errors in sequential programs can lead to security exploits, errors in concurrent programs can lead to concurrency attacks. In this paper, we present an in-depth study of concurrency attacks and how they may affect existing defenses. Our study yields several interesting findings. For instance, we find that concurrency attacks can corrupt non-pointer data, such as user identifiers, which existing memory-safety defenses cannot handle. Inspired by our findings, we propose new defense directions and fixes to existing defenses

Concurrency attacks

Just as errors in sequential programs can lead to security exploits, errors in concurrent programs can lead to concurrency attacks. In this paper, we present an in-depth study of concurrency attacks and how they may affect existing defenses. Our study yields several interesting findings. For instance, we find that concurrency attacks can corrupt non-pointer data, such as user identifiers, which existing memory-safety defenses cannot handle. Inspired by our findings, we propose new defense directions and fixes to existing defenses.

(Self-)Surveillance, Anti-Doping, and Health in Non-Elite Road Running

This article explores disciplining effects of current anti-doping surveillance systems on the health consequences of non-elites' daily behaviors and habits. As they are left out of direct anti-doping testing and enforcement, it is tempting to argue non-elites are unaffected by the anti-doping efforts focused on the elite level of their sport. However, it is because they are not subject to anti-doping surveillance systems nor forced to comply with anti-doping regulations that non-elites are implicated within the wider arena of disciplinary power that envelops both elite and non-elite athletes and anti-doping agencies (Foucault 1979). Drawing on data from 28 interviews with non-elite runners, I argue these runners do conform to the rules and norms of their sport as far as they understand them, but their knowledge of banned substances is inadequate and many non-elite runners have only a superficial and sometimes incorrect understanding of doping. Many view doping and its associated health risks as a problem only of elite running, as well as a problem limited to only a handful of widely publicized performance enhancing drugs or doping methods. As a result of these misunderstandings non-elite runners are vulnerable to negative health effects of over the counter (OTC) medications and nutritional supplements, which they view as " safe " and part of normal training as a result of the current elite surveillance model of anti-doping. The recent death of a non-elite marathon runner linked to use of the unregulated energy supplement DMAA demonstrates that questionable products are used by runners who may not be fully aware of the risks of use

Toward fairness in artificial intelligence for medical image analysis: Identification and mitigation of potential biases in the roadmap from data collection to model deployment

Purpose: To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups. Approach: Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development. Results: Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies. Conclusions: Our findings provide a valuable resource to researchers, clinicians, and the public at large.</p

Gopi: compiling linear and static channels in go

PTDC/CCI-COM/32166/2017We identify two important features to enhance the design of communication protocols specified in the pi-calculus, that are linear and static channels, and present a compiler, named GoPi, that maps high level specifications into executable Go programs. Channels declared as linear are deadlock-free, while the scope of static channels, which are bound by a hide declaration, does not enlarge at runtime; this is enforced statically by means of type inference, while specifications do not include annotations. Well-behaved processes are transformed into Go code that supports non-deterministic synchronizations and race-freedom. We sketch two main examples involving protection against message forwarding, and forward secrecy, and discuss the features of the tool, and the generated code. We argue that GoPi can support academic activities involving process algebras and formal models, which range from the analysis and testing of concurrent processes for research purposes to teaching formal languages and concurrent systems.publishersversionpublishe