Fidelius: Protecting User Secrets from Compromised Browsers

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features

Effect of surgical experience and spine subspecialty on the reliability of the {AO} Spine Upper Cervical Injury Classification System

OBJECTIVE The objective of this paper was to determine the interobserver reliability and intraobserver reproducibility of the AO Spine Upper Cervical Injury Classification System based on surgeon experience (< 5 years, 5–10 years, 10–20 years, and > 20 years) and surgical subspecialty (orthopedic spine surgery, neurosurgery, and "other" surgery). METHODS A total of 11,601 assessments of upper cervical spine injuries were evaluated based on the AO Spine Upper Cervical Injury Classification System. Reliability and reproducibility scores were obtained twice, with a 3-week time interval. Descriptive statistics were utilized to examine the percentage of accurately classified injuries, and Pearson’s chi-square or Fisher’s exact test was used to screen for potentially relevant differences between study participants. Kappa coefficients (κ) determined the interobserver reliability and intraobserver reproducibility. RESULTS The intraobserver reproducibility was substantial for surgeon experience level (< 5 years: 0.74 vs 5–10 years: 0.69 vs 10–20 years: 0.69 vs > 20 years: 0.70) and surgical subspecialty (orthopedic spine: 0.71 vs neurosurgery: 0.69 vs other: 0.68). Furthermore, the interobserver reliability was substantial for all surgical experience groups on assessment 1 (< 5 years: 0.67 vs 5–10 years: 0.62 vs 10–20 years: 0.61 vs > 20 years: 0.62), and only surgeons with > 20 years of experience did not have substantial reliability on assessment 2 (< 5 years: 0.62 vs 5–10 years: 0.61 vs 10–20 years: 0.61 vs > 20 years: 0.59). Orthopedic spine surgeons and neurosurgeons had substantial intraobserver reproducibility on both assessment 1 (0.64 vs 0.63) and assessment 2 (0.62 vs 0.63), while other surgeons had moderate reliability on assessment 1 (0.43) and fair reliability on assessment 2 (0.36). CONCLUSIONS The international reliability and reproducibility scores for the AO Spine Upper Cervical Injury Classification System demonstrated substantial intraobserver reproducibility and interobserver reliability regardless of surgical experience and spine subspecialty. These results support the global application of this classification system

Prospects of Silk Sericin as an Adsorbent for Removal of Ibuprofen from Aqueous Solution

This Article presents removal of ibuprofen from aqueous solution using commercially available silk sericin as an adsorbent in an integrated adsorbent-membrane process. The adsorption study was performed at different physiological conditions, such as adsorbent concentration (1–10g), ibuprofen concentration (10–70 mg/L), temperatures (20, 30, and 40 °C), and pH (5, 6, 7, and 8). The occurrence of adsorption before membrane separation was confirmed by performing analysis of sericin–ibuprofen interaction and complex formation. Sericin–ibuprofen interaction and complex formation was investigated using FTIR, FESEM, fluorescence spectroscopy, XRD, and ITC. Sericin and ibuprofen interaction is spontaneous and endothermic in nature, while random coil transition of Sericin governed the adsorption system. ITC analysis exhibited a binding affinity (<i>K</i>_b) value of 2.51 × 10⁴ ± 1.4 and one binding site (<i>n</i> ≈ 1) per molecule at 27 °C, revealing moderate binding of ibuprofen to the sericin protein. Complete removal of ibuprofen (at 10 mg/L, pH 8) was achieved using 10 g of sericin (pH 4) and at a temperature of 40 °C in reverse osmosis membrane process. The results established in this work concludes that sericin may be used as an adsorbent for the removal of micropollutants, such as ibuprofen from drinking water

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale