Ankur Chattopadhyay

Bio: Ankur Chattopadhyay is an academic researcher from Northern Kentucky University. The author has contributed to research in topics: Outreach & Experiential learning. The author has an hindex of 4, co-authored 27 publications receiving 155 citations. Previous affiliations of Ankur Chattopadhyay include University of Colorado Colorado Springs & Adams State University.

PrivacyCam: a Privacy Preserving Camera Using uCLinux on the Blackfin DSP

Abstract: Considerable research work has been done in the area of surveillance and biometrics, where the goals have always been high performance, robustness in security and cost optimization With the emergence of more intelligent and complex video surveillance mechanisms, the issue of "privacy invasion" has been looming large Very little investment or effort has gone into looking after this issue in an efficient and cost-effective way The process of PICO (privacy through invertible cryptographic obscuration) is a way of using cryptographic techniques and combining them with image processing and video surveillance to provide a practical solution to the critical issue of "privacy invasion" This paper presents the idea and example of a realtime embedded application of the PICO technique, using uCLinux on the tiny Blackfin DSP architecture, along with a small Omnivision camera It demonstrates how the practical problem of "privacy invasion" can be successfully addressed through DSP hardware in terms of smallness in size and cost optimization After review of previous applications of "privacy protection", and system components, we discuss the "embedded jpeg-space" detection of regions of interest and the real time application of encryption techniques to improve privacy while allowing general surveillance to continue The resulting approach permits full access (violation of privacy) only by access to the private-key to recover the decryption key, thereby striking a fine trade-off among privacy, security, cost and space

A Middle-School Module for Introducing Data-Mining, Big-Data, Ethics and Privacy Using RapidMiner and a Hollywood Theme

Abstract: Today's organizations, including online businesses, use the art of data-driven decision-making i.e. business-intelligence (BI) to benefit from all the data out in the open. Given the current market demand for BI skill-sets, including the knowledge of different sources and tools for data-collection plus processing, today's youth need a basic understanding of data-driven intelligence, and an awareness of big-data related ethics and privacy. However, there has been limited research and development work towards designing an effective educational module in this regard at the K-12 level. We intend to address this particular limitation by presenting a uniquely engaging middle-school learning module based upon a combination of useful topics, like data-mining, predictive-analytics, data-visualization, big-data, ethics and privacy, using the free RapidMiner software-tool. The novelty of our module lies in the use of a GUI-based visual hands-on platform (RapidMiner), a Hollywood movie-theme based educational activity, as well as an added focus on big-data ethics and privacy, and its conceptual mapping to the NSA-GenCyber security-first principles. We discuss and analyze the survey data obtained from over hundred participants through several offerings of our module as an educational workshop through our Google-IgniteCS and NSA-GenCyber programs. The collected learning-analytics data indicate that our module can become a simple yet effective means for introducing data-mining, big-data, ethical and privacy issues, and GenCyber security-first principles at the middle-school level. Our results show prospects of motivating middle-school participants towards further learning of topics in data-science, data-ethics and data-security, which is necessary today in a variety of professions.

Assessing the Alignment of Social Robots with Trustworthy AI Design Guidelines: A Preliminary Research Study

Abstract: The last few years have seen a strong movement supporting the need of having intelligent consumer products align with specific design guidelines for trustworthy artificial intelligence (AI). This global movement has led to multiple institutional recommendations for ethically aligned trustworthy design of the AI driven technologies, like consumer robots and autonomous vehicles. There has been prior research towards finding security and privacy related vulnerabilities within various types of social robots. However, none of these previous works has studied the implications of these vulnerabilities in terms of the robot design aligning with trustworthy AI. In an attempt to address this gap in existing literature, we have performed a unique research study with two social robots - Zumi and Cozmo. In this study, we have explored flaws within the robot's system, and have analyzed these flaws to assess the overall alignment of the robot system design with the IEEE global standards on the design of ethically aligned trustworthy autonomous intelligent systems (IEEE A/IS Standards). Our initial research shows that the vulnerabilities and design weaknesses, which we found in these robots, can lead to hacking, injection attacks, and other malfunctions that might affect the technology users negatively. We test the intelligent functionalities in these robots to find faults, and conduct a preliminary examination of how these flaws can potentially result in non-adherence with the IEEE A/IS principles. Through this novel study, we demonstrate our approach towards determining alignment of social robots with benchmarks for trustworthy AI, thereby creating a case for prospective design improvements to address unique risks leading to issues with robot ethics and trust.

Secure Coding and Ethical Hacking Workshops with NAO for Engaging K-12 Female Students in CS

Abstract: This research work is based upon the new Microsoft DigiSpark outreach project initiated by the computer science (CS) program at the University of Wisconsin - Green Bay (UWGB) in an effort to engage K-12 female learners in CS. It describes our unique experiential learning approach combining robotics and cybersecurity, which has been adopted in the outreach workshop sessions as part of our project. Our project involves the use of the NAO humanoid robots in order to successfully engage middle-school and high-school female students, who are underrepresented in CS. These outreach workshop sessions have exposed the young female participants to defensive computer programming, and computing security topics by providing them the hands-on opportunity to write secure code for programming the NAO, and to carry out an ethical hack on the NAO in order to explore robotic system vulnerabilities. Existing literature shows that there is limited work on the efficacy of teaching secure-coding and ethical-hacking related computer-security topics using a robotic-platform. Prior work indicate that a robotic-platform can be an effective and engaging medium for teaching computer-security. However, our proposed approach of teaching secure coding and ethical hacking through hands-on exercises with NAO is a first-of-its-kind experiment with female students at the K-12 level. As preliminary evidence of the effectiveness and potential of this novel approach, we discuss our preliminary experimental results, including initial participant interests and survey data, from the multiple NAO secure coding and NAO ethical hacking workshop sessions, which we have conducted with high-school and middle-school female learners.

A Novel Visual Recognition-basedAuthentication Model Using a Hybrid TrustTheme to Verify Provider Profilesfor Enhancing Information Assurancein Online Healthcare

Abstract: As the number of online healthcare consumers grows day by day, we notice a corresponding rise in the quantity of online healthcare information (OHI), as provided by a large number of different healthcare related web service providers, including several third-party websites, like HealthGrades.com, ZocDoc.com, and ShareCare.com. Given the convenience and ease of having OHI at their disposal through web browsing, today’s healthcare often resort to making “Dr. Google” their first point of contact instead of an actual physician. However, this may lead to cyber psychological issues, like cyberchondria, which are related to uncertainty, mental anxiety, and credibility concerns in regard to OHI. Existing literature shows that there been some research work done on the challenges posed by cyberchondria like cyber psychological issues, especially in non-computing disciplines. However, to our knowledge, the research work, as presented here, is the first of its kind to directly propose Journal of Cyber Security and Mobility, Vol. 7 3, 1–46. River Publishers doi: 10.13052/jcsm2245-1439.733 This is an Open Access publication. c © 2018 the Author(s). All rights reserved. 2 A. Chattopadhyay et al. an information assurance driven technical computing solution to this interdisciplinary problem. None of the previous research work has proposed to address the reliability issues associated with OHI by handling multi-layered trust antecedents from different domains at both the website and the organizational levels. Additionally, the OHI research in these efforts have not cumulatively accounted for multi-level factors like security, assurance, social presence, verification, reputation, and familiarity, which together contribute towards building trust for countering cyberchondria. Hence, in order to enhance the process of trust formation for OHI, we conceptualize and propose a novel hybrid is driven by biometric authentication of physician image profiles. Our proposed approach uniquely addresses the multi-dimensional and social aspects of OHI related trust building, including interpersonal equations of both the patient and the physician at the website as well as the institutional levels. One of the major contributions of this work is proposition of a hybrid, multilayered analysis model for OHI based trust computing that includes a unique, improvised amalgamation of different trust factors from interdisciplinary and disciplinary research domains, including information assurance and security. The uniqueness of this model lies in its biometrics-inspired basis, along with its hybrid trust focus with a fine blend of soft trust and hard trust approaches. As part of our research investigation with this proposed approach, an experiment has been conducted with a unique set of about close to seventy (70) OHI website based physician visual profiles in order to demonstrate a potential implementation of this trust-computing model. Another significant contribution of this research is the creation of the first of its kind unique dataset of acceptable physician profile images from various OHI websites in relation to testing our proposed OHI trust-computing model based approach. We see this research work as a novel initiative for improving OHI credibility in an effort to set up a prospective benchmark pathway towards a new multi-dimensional OHI trust metric for addressing cyberchondria like cyber psychological issues. The entire collection of our varied experimental results from this research are shared and reported as part of this paper. We believe that this work will drive further innovative research experiments with the novel OHI trust computing model, as proposed in this paper, and shall form the basis of future trust computing research towards finding ways to mitigate cyber psychological issues in the realm of OHI.

An Introduction to Distributed Smart Cameras

Abstract: Distributed smart cameras (DSCs) are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Processing images in a network of distributed smart cameras introduces several complications. However, we believe that the problems DSCs solve are much more important than the challenges of designing and building a distributed video system. We argue that distributed smart cameras represent key components for future embedded computer vision systems and that smart cameras will become an enabling technology for many new applications. We summarize smart camera technology and applications, discuss current trends, and identify important research challenges.

Scalable crowd-sourcing of video from mobile devices

Abstract: We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture, GigaSight, is effectively a Content Delivery Network (CDN) in reverse. It achieves scalability by decentralizing the collection infrastructure using cloudlets based on virtual machines~(VMs). Based on time, location, and content, privacy sensitive information is automatically removed from the video. This process, which we refer to as denaturing, is executed in a user-specific VM on the cloudlet. Users can perform content-based searches on the total catalog of denatured videos. Our experiments reveal the bottlenecks for video upload, denaturing, indexing, and content-based search. They also provide insight on how parameters such as frame rate and resolution impact scalability.

Visual privacy protection methods

Abstract: This work is the first comprehensive review about visual privacy protection.Several categories are established for all of the available privacy protection methods.A review of intelligent monitoring systems that takes privacy into account is provided.An overall vision of the state of the art is given to introduce novel researchers.A critical discussion about relevant key topics and open issues is provided. Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.

Security and Privacy Protection in Visual Sensor Networks: A Survey

Abstract: Visual sensor networks (VSNs) are receiving a lot of attention in research, and at the same time, commercial applications are starting to emerge. VSN devices come with image sensors, adequate processing power, and memory. They use wireless communication interfaces to collaborate and jointly solve tasks such as tracking persons within the network. VSNs are expected to replace not only many traditional, closed-circuit surveillance systems but also to enable emerging applications in scenarios such as elderly care, home monitoring, or entertainment. In all of these applications, VSNs monitor a potentially large group of people and record sensitive image data that might contain identities of persons, their behavior, interaction patterns, or personal preferences. These intimate details can be easily abused, for example, to derive personal profiles. The highly sensitive nature of images makes security and privacy in VSNs even more important than in most other sensor and data networks. However, the direct use of security techniques developed for related domains might be misleading due to the different requirements and design challenges. This is especially true for aspects such as data confidentiality and privacy protection against insiders, generating awareness among monitored people, and giving trustworthy feedback about recorded personal data—all of these aspects go beyond what is typically required in other applications. In this survey, we present an overview of the characteristics of VSN applications, the involved security threats and attack scenarios, and the major security challenges. A central contribution of this survey is our classification of VSN security aspects into data-centric, node-centric, network-centric, and user-centric security. We identify and discuss the individual security requirements and present a profound overview of related work for each class. We then discuss privacy protection techniques and identify recent trends in VSN security and privacy. A discussion of open research issues concludes this survey.

De-identification for privacy protection in multimedia content

Abstract: Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. De-identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents. Privacy protection in multimedia.Taxonomy of the personal identifiers in multimedia contents.De-identification of non-biometrical identifiers.De-identification of physiological, behavioural and soft biometric identifiers.