PSImple: Practical Multiparty Maliciously-Secure Private Set Intersection

Private set intersection (PSI) protocols allow a set of mutually distrustful parties, each holding a private set of items, to compute the intersection over all their sets, such that no other information is revealed. PSI has a wide variety of applications including online advertising (e.g., efficacy computation), security (e.g., botnet detection, intrusion detection), proximity testing (e.g., COVID-19 contact tracing), and more. Private set intersection is a rapidly developing area and there exist many highly efficient protocols. However, almost all of these protocols are for the case of two parties or for semi-honest security. In particular, despite the high interest in this problem, prior to our work there has been no concretely efficient, maliciously secure multiparty PSI protocol. We present PSImple, the first concretely efficient maliciously-secure multiparty PSI protocol. Our construction is based on oblivious transfer and garbled Bloom filters, and has a round-optimal online phase. To demonstrate the practicality of PSImple, we implemented it and ran experiments with up to 32 parties and 220 inputs. We show that PSImple is competitive even with the state-of-the-art concretely efficient semi-honest multiparty PSI protocols. Additionally, we revisit the garbled Bloom filter parameters used in the 2-party PSI protocol of Rindal and Rosulek (Eurocrypt 2017). Using a more careful analysis, we show that the size of the garbled Bloom filters and the number of oblivious transfers required for malicious security can be significantly reduced, often by more than 20%. These improved parameters also imply a better security guarantee, and can be used both in the 2-party PSI protocol of Rindal and Rosulek and in PSImple.

A hybrid deep learning based approach for the prediction of social distancing among individuals in public places during Covid19 pandemic

Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals. [ FROM AUTHOR]

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic.

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

Gender and class in the imagined educational spaces in Pakistani primary school textbooks: a challenge for inclusivity agenda

Post-COVID educational planning demands an urgent re-evaluation of the inclusivity of our educational systems, now that almost 24 million learners, a majority of these girls and the poor in developing countries, are at the risk of dropping out. This paper explores the discursive inclusivity of some primary level textbooks used in government and low-fee private schools in Pakistan. An analysis of the intersection of gender and class in 38 ‘imagined' educational spaces (classrooms/schools and related contexts) appearing in the textbooks revealed that the discourse strongly excluded and legitimized the absence of working class females, while marginalizing those from middle class. Education appears to be largely irrelevant to the lives of females across classes, just as they themselves appear to be knowledge construction. The normalized presence in education is that of middle-class males, with some peripheral space for those from working-class males. Foucault's theoretical framework reveals exclusionary techniques of: a) spatial exclusion b) exclusivity of the right to speak what counts as knowledge;c) construction of differential enabling possibilities. Although transgressive at times, the discourse never challenges the dominant norms, highlighting a worrisome aspect in textbooks that need to be addressed by policy makers and educationists. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Impacts of Covid-19 Lockdown on Concentration Levels of Traffic-Related Air Pollutants in Ibadan-a West African City

Trends and sources of air pollution at twenty-five traffic Intersections (TIs) before and during covid-19 lockdown were investigated in Ibadan, Nigeria. The relationships among climatic parameters, vehicular counts and ten air pollutants which includes particulate matter (PM1, PM2.5, PM10 & Total Suspended Particles-TSP) and gaseous pollutants (CO, NO2, SO2, NH3, total volatile organic compounds-TVOCs, and ground level O3) measured simultaneously at TIs were analysed. Results indicated significant decrease in mean concentrations of all pollutants studied except NO2 with 212% increase during the study period. Concentrations of gaseous pollutants CO, SO2, NH3, TVOCs and ground level O3 reduced by 7.92%, 24.80%, 1.58%, 44.08% and 4.28%, respectively while particulates concentrations of PM1, PM2.5, PM10 and TSP concentrations decreased by 49.64%, 60.79%, 81.21% and 84.17%, respectively during lockdown. An integrated source apportionment approach using Pearson’s correlation, Airflow backward trajectories arriving in the study area and Principal component analysis (PCA) identified vehicular emission as the primary source of studied air pollutants at TIs before and during lockdown in Ibadan. Emission from residences, roadside fuel combustion and local air transport of pollutants from nearby upwind areas with industries and farming activities were identified as secondary sources of air pollution affecting the study area. © 2022, University of Ilorin, Faculty of Engineering and Technology. All rights reserved.

A hybrid deep learning based approach for the prediction of social distancing among individuals in public places during Covid19 pandemic

Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

PSImple: Practical Multiparty Maliciously-Secure Private Set

Private set intersection (PSI) protocols allow a set of mutually distrustful parties, each holding a private set of items, to compute the intersection over all their sets, such that no other information is revealed. PSI has a wide variety of applications including online advertising (e.g., efficacy computation), security (e.g., botnet detection, intrusion detection), proximity testing (e.g., COVID-19 contact tracing), and more. Private set intersection is a rapidly developing area and there exist many highly efficient protocols. However, almost all of these protocols are for the case of two parties or for semi-honest security. In particular, despite the high interest in this problem, prior to our work there has been no concretely efficient, maliciously secure multiparty PSI protocol. We present PSImple, the first concretely efficient maliciously-secure multiparty PSI protocol. Our construction is based on oblivious transfer and garbled Bloom filters, and has a round-optimal online phase. To demonstrate the practicality of PSImple, we implemented it and ran experiments with up to 32 parties and 2 20 inputs. We show that PSImple is competitive even with the state-of-the-art concretely efficient semi-honest multiparty PSI protocols. Additionally, we revisit the garbled Bloom filter parameters used in the 2-party PSI protocol of Rindal and Rosulek (Eurocrypt 2017). Using a more careful analysis, we show that the size of the garbled Bloom filters and the number of oblivious transfers required for malicious security can be significantly reduced, often by more than 20%. These improved parameters also imply a better security guarantee, and can be used both in the 2-party PSI protocol of Rindal and Rosulek and in i>PSImple. © 2022 ACM.

PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment

Existing Bluetooth-based private contact tracing (PCT) systems can privately detect whether people have come into direct contact with patients with COVID-19. However, we find that the existing systems lack functionality and flexibility, which may hurt the success of contact tracing. Specifically, they cannot detect indirect contact (e.g., people may be exposed to COVID-19 by using a contaminated sheet at a restaurant without making direct contact with the infected individual);they also cannot flexibly change the rules of "risky contact,"such as the duration of exposure or the distance (both spatially and temporally) from a patient with COVID-19 that is considered to result in a risk of exposure, which may vary with the environmental situation.In this article, we propose an efficient and secure contact tracing system that enables us to trace both direct contact and indirect contact. To address the above problems, we need to utilize users' trajectory data for PCT, which we call trajectory-based PCT. We formalize this problem as a spatiotemporal private set intersection that satisfies both the security and efficiency requirements. By analyzing different approaches such as homomorphic encryption, which could be extended to solve this problem, we identify the trusted execution environment (TEE) as a candidate method to achieve our requirements. The major challenge is how to design algorithms for a spatiotemporal private set intersection under the limited secure memory of the TEE. To this end, we design a TEE-based system with flexible trajectory data encoding algorithms. Our experiments on real-world data show that the proposed system can process hundreds of queries on tens of millions of records of trajectory data within a few seconds. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Soft hypergraph for modeling global interactions via social media networks

Soft set theory is a map of a set of parameters to the subsets of a universe which can be utilized to parametrically model the uncertainty. On the other hand, Graph (hypergraph) theory is used to simplify some practical problems. Inspired by these concepts, the notion of “soft hypergraph” is developed as the generalization of both soft graph and hypergraph for important application in social media networking. Based on the structure of soft hypergraph, various techniques and operations are provided including soft sub-hypergraph, extended union, extended intersection, cartesian product and complement with elucidatory examples. As per the current global spread of COVID, most of the national and international interactions and social affairs have been virtually conducted via social media networks, such as Skype, Microsoft Teams, WhatsApp, Telegram, Zoom, Instagram, WeChat, etc. For the purpose of Intelligent management of network systems, we use the “generalized soft hypergraph” to model the global e-communication networking of individuals in online platforms. © 2022 Elsevier Ltd

Circuit-based PSI for Covid-19 Risk Scoring

Many solutions have been proposed to improve manual contact tracing for infectious diseases through automation. Privacy is crucial for the deployment of such a system as it greatly influences adoption. Approaches for digital contact tracing like Google Apple Exposure Notification (GAEN) protect the privacy of users by decentralizing risk scoring. But GAEN leaks information about diagnosed users as ephemeral pseudonyms are broadcast to everyone. To combat deanonymisation based on the time of encounter while providing extensive risk scoring functionality we propose to use a private set intersection (PSI) protocol based on garbled circuits. Using oblivious programmable pseudo random functions PSI (OPPRF-PSI) , we implement our solution CERTAIN which leaks no information to querying users other than one risk score for each of the last 14 days representing their risk of infection. We implement payload inclusion for OPPRF-PSI and evaluate the efficiency and performance of different risk scoring mechanisms on an Android device.

Research on the Relationship between Mobile Network Data and COVID-19 Cases

During this nearly two-years-long pandemic period, the COVID-19 impacts people's lives dramatically, many people were forced to stay at home by the government's lockdown policy, and they also need to work and study at home. Therefore, there is an equivalent impact on networks as people are more dependent on them. But there are only a limited number of research has been done in this intersection area between the pandemic and networks. So, we want to fill this gap. In this paper, we will study the mobile network data from U.S. Federal Communications Commission (FCC) and COVID-19 cases data from the U.S. centers for disease control and prevention (CDC), then use machine learning to investigate the relationship between mobile network data and COVID-19 cases. We will discuss other related works, which used other methods or investigated this topic in other regions, then we will introduce our machine learning methods, experiments and give the conclusion. © 2022 IEEE.

AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT Images in COVID-19 Patients

COVID-19 pandemic is a deadly disease spreading very fast. People with the confronted immune system are susceptible to many health conditions. A highly significant condition is pneumonia, which is found to be the cause of death in the majority of patients. The main purpose of this study is to find the volume of GGO and consolidation of a COVID-19 patient, so that the physicians can prioritize the patients. Here, we used transfer learning techniques for segmentation of lung CTs with the latest libraries and techniques which reduces training time and increases the accuracy of the AI Model. This system is trained with DeepLabV3 + network architecture and model ResNet50 with ImageNet weights. We used different augmentation techniques like Gaussian noise, horizontal shift, color variation, etc., to get to the result. Intersection over Union (IoU) is used as the performance metrics. The IoU of lung masks is predicted as 99.78% and that of infected masks is as 89.01%. Our work effectively measures the volume of infected region by calculating the volume of infected and lung mask region of the patients. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Traffic Analysis on Intersection Using PTV Vissim

As India is in its developing stage and the traffic on the other side in India is very heterogeneous or mixed in its nature and the average growth rate of vehicles in India is about 8%. With the increase rate of urbanization in India it will lead to the considerable traffic and travel growth on the roads which will result in vehicular delays, long queues and traffic congestion. So, in this paper with the help of traffic simulation software, i.e. VISSIM, three simulation of an unsignalized intersection {Dadour and Una-Jahu, Nerchowk Rd. (NH-21),H.P} will be analyzed and will compare them on the basis of vehicular delays and long queues. These three simulation will be analyzed on the basis of real world traffic data which is less from the expectations due to the pandemic covid-19, theoretical traffic data (increase in real data by 30%) and theoretical traffic data {with traffic signals as theoretical data follows warrant 1 (Min. Vehicular Volume) shown in IRC:93:1985}. Result showed that with increase in vehicular data there was not so much variation in vehicular delays, whereas there was an increase in long queues or queue stops and whilst third simulation (with traffic lights) is done it shows that it overcomes the queue stops of the intersection. © 2022, Springer Nature Singapore Pte Ltd.