Privacy-preserving audit for broker-based health information exchange

Health Information Technology has spurred the development of distributed systems known as Health Information Exchanges (HIEs) to enable the sharing of patient records between different health care organizations. Participants using these exchanges wish to disclose the minimum possible amount of information that is needed due to patient privacy concerns over sensitive medical information. Therefore, broker-based HIEs aim to keep limited information in exchange repositories and to ensure faster and more efficient patient care. It is essential to audit these exchanges carefully to minimize the risk of illegitimate data sharing. This thesis presents a design for auditing broker-based HIEs in a way that controls the information available in audit logs and regulates its release during audit investigations based on the requirements of applicable privacy policy. In our design, we utilized formal rules to verify access to HIE and adopted Hierarchical Identity-Based Encryption (HIBE) to support the staged release of data required for audits and a balance between automated and manual reviews. We test our methodology with a consolidated and centralized audit source that incorporates a standard for auditing HIEs called the Audit Trail and Node Authentication Profile (ATNA) protocol with supplementary audit documentation from HIE participants

Towards More Effective Traffic Analysis in the Tor Network.

University of Minnesota Ph.D. dissertation. February 2021. Major: Computer Science. Advisor: Nicholas Hopper. 1 computer file (PDF); xiii, 161 pages.Tor is perhaps the most well-known anonymous network, used by millions of daily users to hide their sensitive internet activities from servers, ISPs, and potentially, nation-state adversaries. Tor provides low-latency anonymity by routing traffic through a series of relays using layered encryption to prevent any single entity from learning the source and destination of a connection through the content alone. Nevertheless, in low-latency anonymity networks, the timing and volume of traffic sent between the network and end systems (clients and servers) can be used for traffic analysis. For example, recent work applying traffic analysis to Tor has focused on website fingerprinting, which can allow an attacker to identify which website a client has downloaded based on the traffic between the client and the entry relay. Along with website fingerprinting, end-to-end flow correlation attacks have been recognized as the core traffic analysis in Tor. This attack assumes that an adversary observes traffic flows entering the network (Tor flow) and leaving the network (exit flow) and attempts to correlate these flows by pairing each user with a likely destination. The research in this thesis explores the extent to which the traffic analysis technique can be applied to more sophisticated fingerprinting scenarios using state-of-the-art machine-learning algorithms and deep learning techniques. The thesis breaks down four research problems. First, the applicability of machine-learning-based website fingerprinting is examined to a search query keyword fingerprinting and improve the applicability by discovering new features. Second, a variety of fingerprinting applications are introduced using deep-learning-based website fingerprinting. Third, the work presents data-limited fingerprinting by leveraging a generative deep-learning technique called a generative adversarial network that can be optimized in scenarios with limited amounts of training data. Lastly, a novel deep-learning architecture and training strategy are proposed to extract features of highly correlated Tor and exit flow pairs, which will reduce the number of false positives between pairs of flows

DeTorrent: An Adversarial Padding-only Traffic Analysis Defense

While anonymity networks like Tor aim to protect the privacy of their users, they are vulnerable to traffic analysis attacks such as Website Fingerprinting (WF) and Flow Correlation (FC). Recent implementations of WF and FC attacks, such as Tik-Tok and DeepCoFFEA, have shown that the attacks can be effectively carried out, threatening user privacy. Consequently, there is a need for effective traffic analysis defense. There are a variety of existing defenses, but most are either ineffective, incur high latency and bandwidth overhead, or require additional infrastructure. As a result, we aim to design a traffic analysis defense that is efficient and highly resistant to both WF and FC attacks. We propose DeTorrent, which uses competing neural networks to generate and evaluate traffic analysis defenses that insert 'dummy' traffic into real traffic flows. DeTorrent operates with moderate overhead and without delaying traffic. In a closed-world WF setting, it reduces an attacker's accuracy by 61.5%, a reduction 10.5% better than the next-best padding-only defense. Against the state-of-the-art FC attacker, DeTorrent reduces the true positive rate for a $10^{-5}$ false positive rate to about .12, which is less than half that of the next-best defense. We also demonstrate DeTorrent's practicality by deploying it alongside the Tor network and find that it maintains its performance when applied to live traffic.Comment: Accepted to the 24th Privacy Enhancing Technologies Symposium (PETS 2024

"Direct" grafting of linear macromolecular "wedges" to the edge of pristine graphite to prepare edge-functionalized graphene-based polymer composites

The edges of pristine graphite were covalently grafted with para-poly(ether-ketone) (pPEK) in a mildly acidic polyphosphoric acid (PPA)/phosphorus pentoxide (P(2)O(5)) medium. The resulting pPEK grafted graphite (pPEK-g-graphite) showed that the pristine graphite had been exfoliated into a few layers of graphene platelets (graphene-like sheets), which were uniformly dispersed into a pPEK matrix. As a result, the tensile properties of pPEK-g-graphite films were greatly improved compared to those of controlled pPEK films. The origins of these enhanced mechanical properties were deduced from scanning electron microscope (SEM) images of fracture surfaces. Upon tracing wide-angle X-ray scattering (WAXS) patterns of the film under strain, the graphene-like sheets were further exfoliated by an applied shear force, suggesting that a toughening mechanism for the pPEK-g-graphite film occurred. This approach envisions that the "direct'' edge grafting of pristine graphite without pre-treatments such as corrosive oxidation and/or destructive sonication is a simple and efficient method to prepare graphene-based polymer composites with enhanced mechanical properties.close161

What Drives Task Performance During Animal Fluency in People With Alzheimer's Disease?

Background Animal fluency is a widely used task to assess people with Alzheimer's disease (AD) and other neurological disorders. The mechanisms that drive performance in this task are argued to rely on language and executive functions. However, there is little information regarding what specific aspects of these cognitive processes drive performance on this task. Objective To understand which aspects of language (i.e., semantics, phonological output lexicon, phonological assembly) and executive function (i.e., mental set shifting; information updating and monitoring; inhibition of possible responses) are involved in the performance of animal fluency in people with AD. Methods Animal fluency data from 58 people with probable AD from the DementiaBank Pittsburgh Corpus were analyzed. Number of clusters and switches were measured and nine word properties (e.g., frequency, familiarity) for each of the correct words (i.e., each word counting toward the total score, disregarding non-animals and repetitions) were determined. Random forests were used to understand which variables predicted the total number of correct words, and conditional inference trees were used to search for interactions between the variables. Finally, Wilcoxon tests were implemented to cross-validate the results, by comparing the performance of participants with scores below the norm in animal fluency against participants with scores within the norm based on a large normative sample. Results Switches and age of acquisition emerged as the most important variables to predict total number of correct words in animal fluency in people with AD. Cross-validating the results, people with AD whose animal fluency scores fell below the norm produced fewer switches and words with lower age of acquisition than people with AD with scores in the normal range. Conclusion The results indicate that people with AD rely on executive functioning (information updating and monitoring) and language (phonological output lexicon, not necessarily semantics) to produce words on animal fluency

EFFECTS OF SPECIFIC MUSCLE IMBALANCE IMPROVEMENT TRAINING ON THE BALANCE ABILITY IN ELITE FENCERS

The purpose of this study was to investigate the effects of specific muscle imbalance improvement training (SMIIT) on the balance ability. Subjects were 9 male national team fencers with 28.2±2.2 yrs, 182.3±4.0 cm, and 76.5±8.2 kg. The SMIIT included flexibility training, Pilates, muscle balance training and was conducted for 12 weeks with 4 times per week. As a result, there was no significant difference in COM dispersion among static balance maintaining abilities, but reduction in the COP dispersion was shown. In conclusion, SMIIT seemed to affect in improving dynamic balance maintaining abilities especially in non-dominant leg

Crystal Structure of the TLR4-MD-2 Complex with Bound Endotoxin Antagonist Eritoran

SummaryTLR4 and MD-2 form a heterodimer that recognizes LPS (lipopolysaccharide) from Gram-negative bacteria. Eritoran is an analog of LPS that antagonizes its activity by binding to the TLR4-MD-2 complex. We determined the structure of the full-length ectodomain of the mouse TLR4 and MD-2 complex. We also produced a series of hybrids of human TLR4 and hagfish VLR and determined their structures with and without bound MD-2 and Eritoran. TLR4 is an atypical member of the LRR family and is composed of N-terminal, central, and C-terminal domains. The β sheet of the central domain shows unusually small radii and large twist angles. MD-2 binds to the concave surface of the N-terminal and central domains. The interaction with Eritoran is mediated by a hydrophobic internal pocket in MD-2. Based on structural analysis and mutagenesis experiments on MD-2 and TLR4, we propose a model of TLR4-MD-2 dimerization induced by LPS

Triglyceride glucose index predicts coronary artery calcification better than other indices of insulin resistance in Korean adults: the Kangbuk Samsung Health Study

Purpose Insulin resistance is one of the most important mechanisms in the development of diabetes, and it is closely related to the presence and severity of coronary heart disease. Triglyceride glucose (TyG) index is a useful marker of insulin resistance; however, few studies have investigated the relationship between TyG and subclinical atherosclerosis. Therefore, we evaluated the association of TyG and subclinical coronary atherosclerosis as measured by coronary artery calcium score (CACS). Methods Our study included 30,776 participants (mean age of 41 years, 80.4% male) enrolled in a health screening program, in whom CACS were measured. Homeostasis model assessment of insulin resistance (HOMA-IR), TyG index, TyG-body mass index (BMI), and TyG-waist circumference (WC) were subsequently analyzed. Indices were calculated using the following formulae: HOMA-IR=fasting insulin (μU/mL)×fasting plasma glucose (FPG; mmol/L)/22.5; TyG index=Ln [TG (mg/dL)×FPG (mg/dL)/2]; TyG-BMI=TyG index×BMI; and TyG-WC=TyG index×WC. CACS was measured using multidetector computed tomography, and the presence of coronary artery calcification (CAC) was defined by CACS>0. Results The prevalence of CAC was 14.4% in the study population. Multivariate logistic regression analysis showed that participants with TyG-BMI in the highest tertile were 1.638 times more likely to have CAC after adjustment for other metabolic parameters compared with participants with TyG-BMI in the lowest tertile (odds ratio, 1.612; 95% confidence interval, 1.465 to 1.774). The receiver operating characteristics curve for prediction of CAC showed that TyG-WC index had a higher area under the curve (AUC=0.626) than other indices (AUCTyG=0.617, AUCTyG-BMI=0.616, AUCHOMA-IR=0.562). Conclusion TyG index predicted CAC better than other markers of insulin resistance, and could be a useful marker for predicting subclinical atherosclerosis