Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation

The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand

Acting rehearsal in collaborative multimodal mixed reality environments

This paper presents the use of our multimodal mixed reality telecommunication system to support remote acting rehearsal. The rehearsals involved two actors, located in London and Barcelona, and a director in another location in London. This triadic audiovisual telecommunication was performed in a spatial and multimodal collaborative mixed reality environment based on the 'destination-visitor' paradigm, which we define and put into use. We detail our heterogeneous system architecture, which spans the three distributed and technologically asymmetric sites, and features a range of capture, display, and transmission technologies. The actors' and director's experience of rehearsing a scene via the system are then discussed, exploring successes and failures of this heterogeneous form of telecollaboration. Overall, the common spatial frame of reference presented by the system to all parties was highly conducive to theatrical acting and directing, allowing blocking, gross gesture, and unambiguous instruction to be issued. The relative inexpressivity of the actors' embodiments was identified as the central limitation of the telecommunication, meaning that moments relying on performing and reacting to consequential facial expression and subtle gesture were less successful

RFCs, MOOs, LMSs: Assorted Educational Devices\ud

This paper discusses implicit social consequences of four basic internet protocols. The results are then related to the field of computer-assisted teaching. An educational on-line community is described and compared to the emerging standard of web-based learning management.\u

BioVault : a protocol to prevent replay in biometric systems

D.Com. (Informatics)Please refer to full text to view abstrac

Searchable Symmetric Encryption and its applications

In the age of personalized advertisement and online identity profiles, people’s personal information is worth more to corporations than ever. Storing data in the cloud is increasing in popularity due to bigger file sizes and people just storing more information digitally. The leading cloud storage providers require insight into what users store on their servers. This forces users to trust their cloud storage provider not to misuse their information. This opens the possibility that private information is sold to hackers or is made publicly available on the internet. However, the more realistic case is that the service provider sells or misuses your metadata for use in personalized advertisements or other, less apparent purposes. This thesis will explore Searchable Sym- metric Encryption (SSE) algorithms and how we can utilize them to make a more secure cloud storage serviceMasteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Securing Communication Channels in IoT using an Android Smart Phone

In today's world, smart devices are a necessity to have, and represent an essential tool for performing daily activities. With this comes the need to secure the communication between the IoT devices in the consumer's home, to prevent attacks that may jeopardize the confidentiality and integrity of communication between the IoT devices. The life cycle of a a simple device includes a series of stages that the device undergoes: from construction and production to decommissioning. In this thesis, the Manufacturing, Bootstrapping and Factory Reset parts of IoT device's life cycle are considered, focusing on security. For example, the Controller of user's home network (e.g., user's smart phone) should bootstrap the ``right'' IoT device and the IoT device should bootstrap with the ``right'' Controller. The security is based on device credentials, such as the device certificate during the bootstrapping process, and the operational credentials that are provisioned to the IoT device from the Controller during the bootstrapping. The goal of this thesis is to achieve easy-to-use and secure procedure for setting up the IoT device into a home network, and for controlling that IoT device from an Android mobile phone (Controller). The objectives are: (1) explore the different aspects of using a smartphone as a Controller device to securely manage the life cycle of a simple device; (2) propose a system design for securely managing the life cycle of a simple device from a Controller compliant with existing standards, (e.g. Lightweight Machine to Machine (LwM2M) is an industrial standard used to manage and control industrial IoT Devices); (3) implement a proof of concept based on the system design; (4) provide a user-friendly interface for a better experience for the user by using popular bootsrapping methods such as QR code scanning; (5) discuss the choices regarding securing credentials and managing data, and achieve a good balance between usability and security during the bootstrapping process. In order to achieve those goals, the state-of-art technologies for IoT device management were studied. Then an Android application that uses LwM2M standard in consumer's home setting was specified, designed and implemented. The Android application is wrapped in a smooth user interface that allows the user a good experience when attempting to connect and control the target IoT device

Fast embedding for image classification & retrieval and its application to the hostel industry

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking an unseen image input and extracting its features representing the input image. Then, for the classification task, this mathematically measured input is categorized according to established criteria in the server and consequently shows the output as a result. On the other hand, for the retrieval task, the extracted features of an unseen query image are sent to the server to search for the most visually similar images to a given image and retrieve these images as a result. Despite image features could be represented by classical features, artificial intelligence-based features, Convolutional Neural Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless, the high dimensional CNN features have been a challenge in particular for applications on mobile or Internet of Things devices. Therefore, in this thesis, several fast embeddings are explored and proposed to overcome the constraints of low memory, bandwidth, and power. Furthermore, the first hostel image database is created with three datasets, hostel image dataset containing 13,908 interior and exterior images of hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing 972 images and 2,380 images, respectively, of 20 London hostel buildings. The results demonstrate that the proposed fast embeddings such as the application of GHM-Rand operator, GHM-Fix operator, and binary feature vectors are able to outperform or give competitive results to those state-of-the-art methods with a lot less computational resource. Additionally, the findings from a ten-year literature review of CBIR study in the tourism industry could picturize the relevant research activities in the past decade which are not only beneficial to the hostel industry or tourism sector but also to the computer science and engineering research communities for the potential real-life applications of the existing and developing technologies in the field

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods