When private set intersection meets big data : an efficient and scalable protocol

Large scale data processing brings new challenges to the design of privacy-preserving protocols: how to meet the increasing requirements of speed and throughput of modern applications, and how to scale up smoothly when data being protected is big. Efficiency and scalability become critical criteria for privacy preserving protocols in the age of Big Data. In this paper, we present a new Private Set Intersection (PSI) protocol that is extremely efficient and highly scalable compared with existing protocols. The protocol is based on a novel approach that we call oblivious Bloom intersection. It has linear complexity and relies mostly on efficient symmetric key operations. It has high scalability due to the fact that most operations can be parallelized easily. The protocol has two versions: a basic protocol and an enhanced protocol, the security of the two variants is analyzed and proved in the semi-honest model and the malicious model respectively. A prototype of the basic protocol has been built. We report the result of performance evaluation and compare it against the two previously fastest PSI protocols. Our protocol is orders of magnitude faster than these two protocols. To compute the intersection of two million-element sets, our protocol needs only 41 seconds (80-bit security) and 339 seconds (256-bit security) on moderate hardware in parallel mode

New Differential Privacy Communication Pipeline and Design Framework

Organizations started to adopt differential privacy (DP) techniques hoping to persuade more users to share personal data with them. However, many users do not understand DP techniques, thus may not be willing to share. Previous research suggested that the design of DP mechanism communication could influence users' willingness to share data. Based on the prior work, we propose a new communication pipeline that starts by asking users about their privacy concerns and then provides a customized DP mechanism and communication. We also propose a design framework that systemically explores effective communication designs ranging from a text-based high-level description to a step-by-step interactive storyboard. Based on the framework, we created 17 designs and recruited five people to evaluate. Our user study showed that text-based descriptions have the highest clarity in all scenarios, while the step-by-step interactive storyboards have the potential to persuade users to trust central DP. Our future work will optimize the design and conduct a large-scale efficacy study.Comment: poste

Secure Dating with Four or Fewer Cards

In Cornell\u27s “CS4830: Introduction to Cryptography” offered Fall 2015, students are asked to devise a physical secure two-party protocol for computing AND, using 4 cards or fewer. An elegant 5-card scheme was first proposed by Boer et al. Recently, in Asiacrypt 2012, Mizuki et al. were the first to improve the scheme to 4 cards. Although they mention that 4 cards is the minimum -- the minimum only holds when users must encode their input each with two cards. Given the collective wisdom of our Cornell CS4830 students, we demonstrate an array of creative schemes using from 1 to 4 cards. Our students documented these solutions in a homework assignment, many of which are unanticipated by the instructor and the TAs. We had fun with students\u27 solutions and therefore would like to share them. Several of the students solutions are simpler than the standard textbook version by Boer et al., and we imagine that they could be useful for pedagogical purposes

Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts

Emerging smart contract systems over decentralized cryp- tocurrencies allow mutually distrustful parties to transact safely with each other without trusting a third-party inter- mediary. In the event of contractual breaches or aborts, the decentralized blockchain ensures that other honest parties obtain commesurate remuneration. Existing systems, how- ever, lack transactional privacy. All transactions, including flow of money between pseudonyms and amount trasacted, are exposed in the clear on the blockchain. We present Hawk, a decentralized smart contract system that does not store financial transactions in the clear on the blockchain, thus retaining transactional privacy from the public’s view. A Hawk programmer can write a private smart contract in an intuitive manner without having to implement cryptography, and our compiler automatically generates an efficient cryptographic protocol where contractual parties in- teract with the blockchain, using cryptographic primitives such as succint zero-knowledge proofs. To formally define and reason about the security of our protocols, we are the first to formalize the blockchain model of secure computation. The formal modeling is of indepen- dent interest. We advocate the community to adopt such a formal model when designing interesting applications atop decentralized blockchains

Designing AI Interfaces for Children with Special Needs in Educational Contexts

The IDC research community has a growing interest in designing AI interfaces for children with special educational needs. Nonetheless, little research has explored the research and design issues, rationale, challenges, and opportunities in this field. Therefore, we propose to host a half-day workshop to bring together researchers and practitioners from the Learning & Education, Accessibility, and Intelligent User Interfaces sub-fields to discuss and identify existing design issues, challenges, and collaboration barriers, to establish consensus on the design of a pragmatic framework, as well as explore future innovation and research opportunities. We aim to foster mutual unders

Longitudinal white-matter abnormalities in sports-related concussion: A diffusion MRI study

Objective To study longitudinal recovery trajectories of white matter after sports-related concussion (SRC) by performing diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. Methods Collegiate athletes (n = 219, 82 concussed athletes, 68 contact-sport controls, and 69 non–contact-sport controls) were included from the Concussion Assessment, Research and Education Consortium. The participants completed clinical assessments and DTI at 4 time points: 24 to 48 hours after injury, asymptomatic state, 7 days after return-to-play, and 6 months after injury. Tract-based spatial statistics was used to investigate group differences in DTI metrics and to identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. Results In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than in the controls at 24 to 48 hours after injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief Symptom Inventory scores and symptom severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non–contact-sport controls. Conclusions Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure. Furthermore, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery tim

Designing Educational Video Games and Intelligent Tutoring Systems for Drill-Based Training

160 pagesDrill and practice is a well-received approach to repeatedly train learners' skills through a series of exercises and to reward them with corrective feedback. However, drill-based training may not improve learners' performance if its exercises are badly designed (e.g., not fun, not relevant to the learning goal, and becoming too difficult or too simple). To make drill-based training more effective, researchers have been designing two types of e-learning tools: educational video games to motivate learners to practice and intelligent tutoring systems to personalize the exercises. Nonetheless, the existing e-learning tools have two main problems: (1) not providing situational context for training, which limits learners' ability to apply previously learned knowledge and skills to real-life situations; (2) not designed for learners with specific learning disabilities (i.e., dyslexia, dyscalculia, and dysgraphia). To design educational video games that provide situational context for training, I sought to leverage entertainment technologies (e.g., storytelling technology) and AI technologies (e.g., computer vision technology). I tackled two specific projects. The first project was to improve anti-phishing training by simulating actual phishing attacks in a role-playing game. The key design challenge was to design an intensive workplace atmosphere that is easy for learners to make mistakes but also keeps the pleasure of completing tasks. My approach was to design an interactive storyline about building business as a banker and to design tragic endings that are realistic but with a sense of black humor. The second project was to teach vocabulary for objects located in the player’s immediate vicinity. The key design challenge was to guide learners to interact with learning materials in the physical world. To meet this challenge, I designed a new selection highlighting mechanics for AR scenes and designed an AR progress bar to indicate players' selection progress. Evaluations showed that my two educational video games for training were fun and also improved learners' post-test performance in practice. To help learners with SLDs use math e-learning tools for math skills training, I started by conducting an interview study with teachers for SLDs to study the difficulties that learners with SLDs faced in using e-learning tools. According to the interview study findings, learners with SLDs needed teachers to help them manage negative emotions during drills and practice. However, teachers were often unavailable during students' independent exercises. Therefore, I designed an intelligent tutoring system that detects and mitigates learners' negative emotional behaviors. This system analyzes eye-gazing data together with other traditional input data to detect learners' negative emotional behaviors. I also designed four intervention methods to mitigate the negative emotional behaviors: (1) praising for correct steps to solve the problem, (2) providing hints, (2) switching to a simpler problem, and (4) offering brain breaks. I conducted a formative study with teachers for SLDs to refine the design of the intelligent tutoring system. Teachers agreed that this system would help learners with SLDs reduce negative emotional behaviors. They also suggested that the system, in the future, should personalize the detection of negative emotional behaviors to help students who have more severe learning disabilities. Overall, my dissertation answered three main research questions: (1) how to design educational video games that provide situational context to practice skills; (2) whether and how learners with SLDs have difficulties in using the existing e-learning tools to practice math skills; and (3) how should we design the e-learning tool to intelligently tutor learners with SLDs. At the end of my dissertation, I discuss the limitations of my doctoral work and propose potential future directions for designing educational video games and intelligent tutoring systems for drill-based training

Intravenous N-acetylcysteine for prevention of contrast-induced nephropathy: a meta-analysis of randomized, controlled trials.

BACKGROUND: Contrast-induced nephropathy (CIN) is one of the common causes of acute renal insufficiency after contrast procedures. Whether intravenous N-acetylcysteine (NAC) is beneficial for the prevention of contrast-induced nephropathy is uncertain. In this meta-analysis of randomized controlled trials, we aimed to assess the efficacy of intravenous NAC for preventing CIN after administration of intravenous contrast media. STUDY DESIGN: Relevant studies published up to September 2012 that investigated the efficacy of intravenous N-acetylcysteine for preventing CIN were collected from MEDLINE, OVID, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, and the conference proceedings from major cardiology and nephrology meetings. The primary outcome was CIN. Secondary outcomes included renal failure requiring dialysis, mortality, and length of hospitalization. Data were combined using random-effects models with the performance of standard tests to assess for heterogeneity and publication bias. Meta-regression analyses were also performed. RESULTS: Ten trials involving 1916 patients met our inclusion criteria. Trials varied in patient demographic characteristics, inclusion criteria, dosing regimens, and trial quality. The summary risk ratio for contrast-induced nephropathy was 0.68 (95% CI, 0.46 to 1.02), a nonsignificant trend towards benefit in patients treated with intravenous NAC. There was evidence of significant heterogeneity in NAC effect across studies (Q = 17.42, P = 0.04; I(2) = 48%). Meta-regression revealed no significant relation between the relative risk of CIN and identified differences in participant or study characteristics. CONCLUSION: This meta-analysis showed that research on intravenous N-acetylcysteine and the incidence of CIN is too inconsistent at present to warrant a conclusion on efficacy. A large, well designed trial that incorporates the evaluation of clinically relevant outcomes in participants with different underlying risks of CIN is required to more adequately assess the role for intravenous NAC in CIN prevention

p21-Activated Kinase 4 Signaling Promotes Japanese Encephalitis Virus-Mediated Inflammation in Astrocytes

Japanese encephalitis virus (JEV) targets central nervous system, resulting in neuroinflammation with typical features of neuronal death along with hyper activation of glial cells. Exploring the mechanisms responsible for the JEV-caused inflammatory response remains a pivotal area of research. In the present study, we have explored the function of p21-activated kinase 4 (PAK4) in JEV-mediated inflammatory response in human astrocytes. The results showed that JEV infection enhances the phosphorylation of PAK4 in U251 cells and mouse brain. Knockdown of PAK4 resulted in decreased expression of inflammatory cytokines that include tumor necrosis factor alpha, interleukin-6, interleukin-1β, and chemokine (C-C motif) ligand 5 and interferon β upon JEV infection, suggesting that PAK4 signaling promotes JEV-mediated inflammation. In addition, we found that knockdown of PAK4 led to the inhibition of MAPK signaling including ERK, p38 MAPK and JNK, and also resulted in the reduced nuclear translocation of NF-κB and phosphorylation of AP-1. These results demonstrate that PAK4 signaling actively promotes JEV-mediated inflammation in human astrocytes via MAPK-NF-κB/AP-1 pathway, which will provide a new insight into the molecular mechanism of the JEV-induced inflammatory response

Meta-regression of Possible Sources of Heterogeneity.

*<p>A negative correlation coefficient implies more benefit as the tested independent variable increases.</p>†<p>For each of these quality components, studies were dichotomized into high or low quality and used through a dummy variable.</p