Objectives The goal of this paper is to provide a compilation of the evidence for the treatment of posttraumatic headache (PTH) in the pediatric population. Headache features and timing of therapy were considered. Background Headache is the most common symptom following mild traumatic brain injury (mTBI), affecting more than 80% of children and adolescents. It is unclear whether treatment for PTH should be tailored based on headache characteristics, particularly the presence of migraine features, and/or chronicity of the headache. Methods Systematic literature searches of PubMed, Embase, Scopus, and Cochrane databases (1985–2021, limited to English) were performed, and key characteristics of included studies were entered into RedCAP® (Prospero ID CRD42020198703). Articles and conference abstracts that described randomized controlled trials (RCTs), cohort studies, retrospective analyses, and case series were included. Participants included youth under 18 years of age with acute (<3 months) and persistent (≥3 months) PTH. Studies that commented on headache improvement in response to therapy were included. Results Twenty‐seven unique studies met criteria for inclusion describing abortive pharmacologic therapies (9), preventative pharmacotherapies (5), neuromodulation (1), procedures (5), physical therapy and exercise (6), and behavioral therapy (2). Five RCTs were identified. Studies that focused on abortive pharmacotherapies were completed in the first 2 weeks post‐mTBI, whereas other treatment modalities focused on outcomes 1 month to over 1‐year post‐injury. Few studies reported on migrainous features (7), personal history of migraine (7), or family history of migraine (3). Conclusions There is limited evidence on the timing and types of therapies that are effective for treating PTH in the pediatric population. Prospective studies that account for headache characteristics and thoughtfully address the timing of therapies and outcome measurement are needed.

Connected robots play a key role in Industry 4.0, providing automation and higher efficiency for many industrial workflows. Unfortunately, these robots can leak sensitive information regarding these operational workflows to remote adversaries. While there exists mandates for the use of end-to-end encryption for data transmission in such settings, it is entirely possible for passive adversaries to fingerprint and reconstruct entire workflows being carried out -establishing an understanding of how facilities operate. In this paper, we investigate whether a remote attacker can accurately fingerprint robot movements and ultimately reconstruct operational workflows. Using a neural network approach to traffic analysis, we find that one can predict TLS-encrypted movements with around ~60% accuracy, increasing to near-perfect accuracy under realistic network conditions. Further, we also find that attackers can reconstruct warehousing workflows with similar success. Ultimately, simply adopting best cybersecurity practices is clearly not enough to stop even weak (passive) adversaries. CCS CONCEPTS• Security and privacy → Systems security; Network security; Side-channel analysis and countermeasures.

We propose VoIPLoc, a novel location fingerprinting technique and apply it to the VoIP call provenance problem. It exploits echo-location information embedded within VoIP audio to support fine-grained location inference. We found consistent statistical features induced by the echo-reflection characteristics of the location into recorded speech. These features are discernible within traces received at the VoIP destination, enabling location inference. We evaluated VoIPLoc by developing a dataset of audio traces received through VoIP channels over the Tor network. We show that recording locations can be fingerprinted and detected remotely with a low false-positive rate, even when a majority of the audio samples are unlabelled. Finally, we note that the technique is fully passive and thus undetectable, unlike prior art. VoIPLoc is robust to the impact of environmental noise and background sounds, as well as the impact of compressive codecs and network jitter. The technique is also highly scalable and offers several degrees of freedom terms of the fingerprintable space. CCS CONCEPTS• Networks → Web protocol security; Network security; • Security and privacy → Pseudonymity, anonymity and untraceability.

The use of connected surgical robotics to automate medical procedures presents new privacy challenges. We argue that conventional patient consent protocols will no longer work. Indeed robots that replace surgeons take on an extraordinary level of responsibility. Human surgeons undergo years of training and peer review in an strongly regulated environment, and they derive trust via a patient's faith in the hospital system beyond the surgeon him/herself. Robots on the other hand derive trust not through the hospital system they operate within, but the integrity of the software that governs their operation. From a privacy perspective, there are two fundamental shifts. First, the threat model has shifted from one where the humans involved were untrusted to one where the robotic software is untrusted. Second, the basic unit of privacy control is no longer a medical record, but is replaced by four new basic units: the subject on which the robot is taking action; the tools used by the robot and the valid form of tool usage; the sensors (i.e data) the robot can access and the operations permitted over them; and, finally access to monitoring and calibration services which afford correct operation of the robot. We suggest that contextual privacy provides useful theoretical tools to solve the privacy problems posed by surgical robots. However, it also poses some challenges: not least that the complexity of the contextual-privacy policies, if rigorously specified to achieve verification and enforceability, will be exceedingly high to directly expose to human regulators/auditors/users that review contextual privacy policies. Another key challenge, is that contextual privacy accommodates information flows but not property. A medical robot works with both information and physical tissues and fluid material. While informational norms allow for judgements about contextual integrity and the transmission principle governs the constraints applied on information transfer, nothing is said about material property. Certainly, contextual privacy provides an anchor for useful notions of privacy in this scenario. However, it appears the to solve the privacy challenges in the field of surgical robotics well, one needs to consider extending contextual privacy to cover both information and material flows.

Distributed sensor networks such as IoT deployments generate large quantities of measurement data. Often, the analytics that runs on this data is available as a web service which can be purchased for a fee. A major concern in the analytics ecosystem is ensuring the security of the data. Often, companies offer Information Rights Management (IRM) as a solution to the problem of managing usage and access rights of the data that transits administrative boundaries. IRM enables individuals and corporations to create restricted IoT data, which can have its flow from organisation to individual control -disabling copying, forwarding, and allowing timed expiry. We describe our investigations into this functionality and uncover a weak-spot in the architecture -its dependence upon the accurate global availability of time.We present an amplified denial-of-service attack which attacks time synchronisation and could prevent all the users in an organisation from reading any sort of restricted data until their software has been re-installed and re-configured. We argue that IRM systems built on current technology will be too fragile for businesses to risk widespread use. We also present defences that leverage the capabilities of Software-Defined Networks to apply a simple filter-based approach to detect and isolate attack traffic.

The supply chain traceability of components from a production facility to deployment and maintenance depends upon its irrefutable identity. There are two well-known methods for identification which includes an identity code stored in the memory and embedding a custom identification hardware. While storing the identity code is susceptible to malicious and unintentional attacks, the approach of embedding a custom identification hardware is infeasible for sensor nodes assembled with Commercially-Off-the-Shelf (COTS) devices. We propose a novel identifier - Acoustic PUF based on the innate properties of the sensor node. Acoustic PUF combines the uniqueness component and the position component of the sensor device signature. The uniqueness component is derived by exploiting the manufacturing tolerances, thus making the signature unclonable. The position component is derived through acoustic fingerprinting, thus giving a sticky identity to the sensor device. We evaluate Acoustic PUF for Uniqueness, Repeatability and Position identity with a deployment spanning several weeks. Through our experimental evaluation and further numerical analysis, we prove that Acoustic PUF can uniquely identify thousands of devices with 99% accuracy while simultaneously detecting the change in position.

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Distributed sensor networks such as IoT deployments generate large quantities of measurement data. Often, the analytics that runs on this data is available as a web service which can be purchased for a fee. A major concern in the analytics ecosystem is ensuring the security of the data. Often, companies offer Information Rights Management (IRM) as a solution to the problem of managing usage and access rights of the data that transits administrative boundaries. IRM enables individuals and corporations to create restricted IoT data, which can have its flow from organisation to individual control-disabling copying, forwarding, and allowing timed expiry. We describe our investigations into this functionality and uncover a weak-spot in the architecture-its dependence upon the accurate global availability of time. We present an amplified denial-of-service attack which attacks time synchronisation and could prevent all the users in an organisation from reading any sort of restricted data until their software has been re-installed and re-configured. We argue that IRM systems built on current technology will be too fragile for businesses to risk widespread use. We also present defences that leverage the capabilities of Software-Defined Networks to apply a simple filter-based approach to detect and isolate attack traffic.