New threats to health data privacy

<p>Abstract</p> <p>Background</p> <p>Along with the rapid digitalization of health data (e.g. Electronic Health Records), there is an increasing concern on maintaining data privacy while garnering the benefits, especially when the data are required to be published for secondary use. Most of the current research on protecting health data privacy is centered around data de-identification and data anonymization, which removes the identifiable information from the published health data to prevent an adversary from reasoning about the privacy of the patients. However, published health data is not the only source that the adversaries can count on: with a large amount of information that people voluntarily share on the Web, sophisticated attacks that join disparate information pieces from multiple sources against health data privacy become practical. Limited efforts have been devoted to studying these attacks yet.</p> <p>Results</p> <p>We study how patient privacy could be compromised with the help of today’s information technologies. In particular, we show that private healthcare information could be collected by aggregating and associating disparate pieces of information from multiple online data sources including online social networks, public records and search engine results. We demonstrate a real-world case study to show user identity and privacy are highly vulnerable to the attribution, inference and aggregation attacks. We also show that people are highly identifiable to adversaries even with inaccurate information pieces about the target, with real data analysis.</p> <p>Conclusion</p> <p>We claim that too much information has been made available electronic and available online that people are very vulnerable without effective privacy protection.</p

Myosin Light Chain Kinase Mediates Intestinal Barrier Disruption following Burn Injury

Background: Severe burn injury results in the loss of intestinal barrier function, however, the underlying mechanism remains unclear. Myosin light chain (MLC) phosphorylation mediated by MLC kinase (MLCK) is critical to the pathophysiological regulation of intestinal barrier function. We hypothesized that the MLCK-dependent MLC phosphorylation mediates the regulation of intestinal barrier function following burn injury, and that MLCK inhibition attenuates the burn-induced intestinal barrier disfunction. Methodology/Principal Findings: Male balb/c mice were assigned randomly to either sham burn (control) or 30 % total body surface area (TBSA) full thickness burn without or with intraperitoneal injection of ML-9 (2 mg/kg), an MLCK inhibitor. In vivo intestinal permeability to fluorescein isothiocyanate (FITC)-dextran was measured. Intestinal mucosa injury was assessed histologically. Tight junction proteins ZO-1, occludin and claudin-1 was analyzed by immunofluorescent assay. Expression of MLCK and phosphorylated MLC in ileal mucosa was assessed by Western blot. Intestinal permeability was increased significantly after burn injury, which was accompanied by mucosa injury, tight junction protein alterations, and increase of both MLCK and MLC phosphorylation. Treatment with ML-9 attenuated the burn-caused increase of intestinal permeability, mucosa injury, tight junction protein alterations, and decreased MLC phosphorylation, but not MLCK expression

Interactive surveillance event detection through mid-level discriminative representation

Event detection from real surveillance videos with complicated background environment is always a very hard task. Different from the traditional retrospective and interactive systems designed on this task, which are mainly executed on video fragments located within the event-occurrence time, in this paper we propose a new interactive system constructed on the mid-level discriminative representations (patches/ shots) which are closely related to the event (might occur beyond the event-occurrence period) and are easier to be detected than video fragments. By virtue of such easilydistinguished mid-level patterns, our framework realizes an effective labor division between computers and human participants. The task of computers is to train classifiers on a bunch of mid-level discriminative representations, and to sort all the possible mid-level representations in the evaluation sets based on the classifier scores. The task of human participants is then to readily search the events based on the clues offered by these sorted mid-level representations. For computers, such mid-level representations, with more concise and consistent patterns, can be more accurately detected than video fragments utilized in the conventional framework, and on the other hand, a human participant can always much more easily search the events of interest implicated by these location-anchored mid-level representations than conventional video fragments containing entire scenes. Both of these two properties facilitate the availability of our framework in real surveillance event detection applications. Copyright is held by the owner/author(s)

A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data

International audienceWe present a new deep learning approach for real-time 3D human action recognition from skeletal data and apply it to develop a vision-based intelligent surveillance system. Given a skeleton sequence, we propose to en- code skeleton poses and their motions into a single RGB image. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the color images to enhance their local patterns and generate more discriminative features. For learning and classification tasks, we design Deep Neural Networks based on the Densely Connected Convolutional Architecture (DenseNet) to extract features from enhanced-color images and classify them into classes. Experi- mental results on two challenging datasets show that the proposed method reaches state-of-the-art accuracy, whilst requiring low computational time for training and inference. This paper also introduces CEMEST, a new RGB-D dataset depicting passenger behaviors in public transport. It consists of 203 untrimmed real-world surveillance videos of realistic normal and anomalous events. We achieve promising results on real conditions of this dataset with the support of data augmentation and transfer learning techniques. This enables the construction of real-world applications based on deep learning for enhancing monitoring and security in public transport