Tanusree Sharma

Bio: Tanusree Sharma is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Computer science & Personally identifiable information. The author has an hindex of 7, co-authored 16 publications receiving 117 citations. Previous affiliations of Tanusree Sharma include Jahangirnagar University.

Use of apps in the COVID-19 response and the loss of privacy protection.

Abstract: Mobile apps provide a convenient source of tracking and data collection to fight against the spread of COVID-19. We report our analysis of 50 COVID-19-related apps, including their use and their access to personally identifiable information, to ensure that the right to privacy and civil liberties are protected.

"It's A Blessing and A Curse": Unpacking Creators' Practices with Non-Fungible Tokens (NFTs) and Their Communities

Abstract: NFTs (Non-Fungible Tokens) are blockchain-based cryptographic tokens to represent ownership of unique content such as images, videos, or 3D objects. Despite NFTs' increasing popularity and skyrocketing trading prices, little is known about people's perceptions of and experiences with NFTs. In this work, we focus on NFT creators and present results of an exploratory qualitative study in which we interviewed 15 NFT creators from nine different countries. Our participants had nuanced feelings about NFTs and their communities. We found that most of our participants were enthusiastic about the underlying technologies and how they empower individuals to express their creativity and pursue new business models of content creation. Our participants also gave kudos to the NFT communities that have supported them to learn, collaborate, and grow in their NFT endeavors. However, these positivities were juxtaposed by their accounts of the many challenges that they encountered and thorny issues that the NFT ecosystem is grappling with around ownership of digital content, low-quality NFTs, scams, possible money laundering, and regulations. We discuss how the built-in properties (e.g., decentralization) of blockchains and NFTs might have contributed to some of these issues. We present design implications on how to improve the NFT ecosystem (e.g., making NFTs even more accessible to newcomers and the broader population).

Enabling User-centered Privacy Controls for Mobile Applications: COVID-19 Perspective

Abstract: Mobile apps have transformed many aspects of clinical practice and are becoming a commonplace in healthcare settings The recent COVID-19 pandemic has provided the opportunity for such apps to play an important role in reducing the spread of the virus Several types of COVID-19 apps have enabled healthcare professionals and governments to communicate with the public regarding the pandemic spread, coronavirus awareness, and self-quarantine measures While these apps provide immense benefits for the containment of the spread, privacy and security of these digital tracing apps are at the center of public debate To address this gap, we conducted an online survey of a midwestern region in the United State to assess people’s attitudes toward such apps and to examine their privacy and security concerns and preferences Survey results from 1,550 participants indicate that privacy/security protections and trust play a vital role in people’s adoption of such apps Furthermore, results reflect users’ preferences wanting to have control over their personal information and transparency on how their data is handled In addition, personal data protection priorities selected by the participants were surprising and yet revealing of the disconnect between technologists and users In this article, we present our detailed survey results as well as design guidelines for app developers to develop innovative human-centered technologies that are not only functional but also respectful of social norms and protections of civil liberties Our study examines users’ preferences for COVID-19 apps and integrates important factors of trust, willingness, and preferences in the context of app development Through our research findings, we suggest mechanisms for designing inclusive apps’ privacy and security measures that can be put into practice for healthcare-related apps, so that timely adoption is made possible

A RESTful E-Governance Application Framework for People Identity Verification in Cloud

Abstract: An effective application framework design for e-governance is definitely a challenging task. The majority of the prior research has focused on designing e-governance architecture where people identity verification takes long time using manual verification system. We develop an efficient application framework that verifies peoples identity. It provides cloud based REST API using deep learning based recognition approach and stores face meta data in neural networks for rapid facial recognition. After each successful identity verification, we store the facial data in the neural network if there is a match between 80–95%. This decreases the error rate in each iteration and enhance the network. Finally, our system is compared with the existing system on the basis of CPU utilization, error rate and cost metrics to show the novelty of this framework. We implement and evaluate our proposed framework which allows any organization and institute to verify people identity in a reliable and secure manner.

Preserving Privacy in Cyber-physical-social systems: An Anonymity and Access Control Approach

Abstract: With the significant development of mobile commerce, the integration of physical, social, and cyber worlds is increasingly common. The term Cyber Physical Social Systems is used to capture technology’s human-centric role. With the revolutionization of CPSS, privacy protections become a major concern for both customers and enterprises. Although data generalization by obfuscation and anonymity can provide protection for an individual’s privacy, overgeneralization may lead to less-valuable data. In this paper, we contrive generalization boundary techniques (k-anonymity) to maximize data usability while minimizing disclosure with a privacy access control mechanism. This paper proposes a combination of purpose-based access control models with an anonymity technique in distributed computing environments for privacy preserving policies and mechanisms that demonstrate policy conflicting problems. This combined approach will provide protections for individual personal information and make data sharable to authorized party with proper purposes. Here, we have examined data with k-anonymity to create a specific level of obfuscation that maintains the usefulness of data and used a heuristic approach to a privacy access control framework in which the privacy requirement is to satisfy the k-anonymity. The extensive experiments on both real-world and synthetic data sets show that the proposed privacy aware access control model with kanonymity is practical and effective. It will generate an anonymized data set in accordance with the privacy clearance of a certain request and allow users access at different privacy levels, fulfilling some set of obligations and addressing privacy and utility requirements, flexible access control, and improved data availability, while guaranteeing a certain level of privacy.

Deep Learning in Mining Biological Data

Abstract: Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps

Abstract: The current COVID-19 crisis has seen governments worldwide mobilising to develop and implement contact-tracing apps as an integral part of their lockdown exit strategies. The challenge facing polic...

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

Abstract: The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Abstract: The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

A Review of Mobile Applications Available in the App and Google Play Stores Used During the COVID-19 Outbreak

Abstract: Purpose The objective of this paper was to review the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, the United Kingdom, USA, and India during the COVID-19 outbreak. Methods This study adopted a systematic search strategy to identify the free mobile applications available in the App and Google Play stores related to the COVID-19 outbreak. According to the PRISMA flowchart of the search, only 12 applications met the inclusion criterion. Results The 12 mobile applications that met the inclusion criterion were: Mawid, Tabaud, Tawakkalna, Sehha, Aarogya setu, TraceTogether, COVID safe, Immuni, COVID symptom study, COVID watch, NHS COVID-19, and PathCheck. The following features and functionalities of the apps were described: app overview (price, ratings, android, iOS, developer/owner, country, status), health tools (user status-risk assessment, self-assessment, E-pass integration, test results reporting, online consultation, contact tracing), learning options (personalized notes, educational resources, COVID-19 information), communication tools (query resolution, appointments, social network, notifications), app design (data visualization, program plan), networking tools (location mapping - GPS, connectivity with other devices), and safety and security options (alerts, data protection). Also, the effectiveness of the apps was analyzed. Conclusion The analysis revealed that various applications have been developed for different functions like contact tracing, awareness building, appointment booking, online consultation, etc. However, only a few applications have integrated various functions and features such as self-assessment, consultation, support and access to information. Also, most of the apps are focused on contact tracing, while very few are dedicated to raising awareness and sharing information about the COVID-19 pandemic. Likewise, the majority of applications rely on GPS and Bluetooth technologies for relevant functions. No apps were identified that had built-in social media features. It is suggested to design and develop an integrated mobile health application with most of the features and functionalities analyzed in this study.