Secure Storage Implementation for Large Files in iOS Environment

The SSL/TLS protocol is the de-facto standard for secure Internet communications, and supported by virtually all modern e-mail clients and Web browsers. With more and more PDAs and cell phones providing wireless e-mail and Web access, there is an increasing demand for establishing secure SSL/TLS connections on devices that are relatively constrained in terms of computational resources. In addition, the cryptographic primitives executed on the client side need to be protected against side-channel analysis since, for example, an attacker may be able to monitor electromagnetic emanations from a mobile device. Using an RSA-based cipher suite has the advantage that all modular exponentiations on the client side are carried out with public exponents, which is uncritical regarding performance and side-channel leakage. However, the current migration to AES-equivalent security levels makes a good case for using an Elliptic Curve Cryptography (ECC)-based cipher suite. We show in this paper that, for high security levels, ECC-based cipher suites outperform their RSA counterparts on the client side, even though they require the integration of diverse countermeasures against side-channel attacks. Furthermore, we propose a new countermeasure to protect the symmetric encryption of messages (i.e. “bulk data”) against Differential Power Analysis (DPA) attacks. This new countermeasure, which we call Inter-Block Shuffling (IBS), is based on an “interleaved” encryption of a number of data blocks using a non-feedback mode of operation (such as counter mode), and randomizes the order in which the individual rounds of the individual blocks are executed. Our experimental results indicate that IBS is a viable countermeasure as it provides good DPA-protection at the expense of a slight degradation in performance.

The work reported here focuses on creating, composing, and recognizing multisensor gestures and their use in terms of remotely controlling another device. The approach allows to compose gestures synthetically, i.e., by recording complex gestures directly as they are performed, and analytically, i.e., by selecting and composing existing gestures to define more complex ones. Analytical gesture composition doesn't have to be sequential: the gestures can be superposed. We explored this in a setting of using a mobile device to control a remote device. In our case, this was a smartphone controlling a computer, which we assume to be most probable setting in practice. For this, a client-server system was developed consisting of the application running on the Android mobile device using the accelerometer, proximity sensor, touchscreen, and ambient light sensor to capture gestures. The server-side application that runs on a computer was developed to store and recognize gestures. The preliminary evaluation results are promising exhibiting the 85% rate in the ability of users to replicate gestures not created by them. According to informal observations, the user seem to be quite comfortable working with their own multisensor gestures.

The latest growth of mobile technology has resulted in more users accessing network services via mobile devices, such as tablets or smartphones. Personalized look and content becomes more important. Mobile devices have different properties than desktop computers. Mobile devices do not have a large screen, convenient input-output devices, and have limited computing capabilities. Content adaptation is a common definition used for adaptation or customization of content transferred from the server via the HTTP protocol to the client. The research method is to adjust the content which will be transferred to the user based on the device resources i.e. the battery on the client side so that higher service quality will be achieved. In this study, researchers will conduct the development of content adaptation methods that consider the condition of battery resources on the devices. Battery resource estimation is done to determine the current condition of existing battery in accessing device. The appropriate content determination mechanism will be based on the value generated from the estimated resource of the battery on the device that has been done.

The popularity of mobile devices has been growing at a very fast rate and it is evident from the fact that it is possessed by almost each and every person and some may have even more than a single mobile device. MCC helps in computation and running of various complex applications on the mobile device and also offloads to the cloud when it requires lot of resources for computation or storage purposes. However, as energy is limited in the mobile device, processing of complex applications using big data is a challenge that needs to be addressed using energy efficient architectures. In this work, we mainly focuses on identifying energy-aware issues for handling big data in Mobile Cloud Computing (MCC) environment and their current solutions. Also, we have included the review of few techniques available to handle big data in mobile devices. This chapter will also include a brief discussion of techniques available to process big data in MCC in an energy efficient manner. Finally, we conclude with an analysis of identified issues for handling big data in MCC and future scope of research.

With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in a field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and perform on-line reasoning about possible threats, based on the information provided by the Social Threat Monitor system developed in the INDECT project.

Efficient mobile AR technology using scalable recognition and tracking based on server-client model

Cloud storage relieves the user from the local management of software and hardware by enabling high quality on demand storage services to store their data in remote locations. Storing the data in remote locations not only reduces the management overheads but also eliminates the user’s physical control over data storage and data security. The loss of physical control over data brings out various security issues such as data loss, tampering of data, data modification. The primary issue is to ensure the correctness of data stored in remote locations which can be resolved by enabling public auditing for cloud storage services. In public auditing, not only the user but anyone can verify the data correctness. The introduction of third-party auditor, who works on behalf of cloud user to verify data integrity helps to reduce user computation resources and cost. The key prerequisite for the public auditing protocol is to support dynamic data operations as data in cloud are dynamic in nature. Cloud is a multiuser environment, so the auditing protocol must scale well to handle multiple auditing processes simultaneously. This paper aims at an extensive survey which mainly outlines the requirements for cloud public auditability and compares varied public auditing protocols.

In this paper, we describe how to improve accessibility for the aurally challenged in a web environment, focusing on utilizing a signing avatar for web pages. Many systems were previously proposed to make a web environment more accessible for the deaf people by providing signed expressions, i.e. translating written text into sign language animations and presenting them in a proper way, based on the observation that deaf users normally have much difficulty understanding text-based information as well as audio contents. We analyze the strengths and weaknesses of these systems with respect to discussed design criteria, and propose a system that presents a signing avatar for web page documents via a mobile device, which is expected to overcome the shortcomings of the previous systems and to improve the accessibility of deaf users to textual contents in a web environment. The proposed system has three main parts based on a client-server architecture: 1) a client that executes a web browser and transmits selected text to the server, 2) a server that takes text as input and translates it into signed expressions through a sign language generation module, and 3) a mobile device that displays signing animation transmitted from the server by streaming. We also present some linguistic issues raised by the difference between Korean and Korean Sign Language. To the best of our knowledge, this is the first approach to the use of a mobile device for web document access by the aurally challenged people. We discuss implications of our study and future directions.

The integral management of facilities located in the subsoil or hidden in walls and ceilings entails numerous challenges in terms of accurate positioning and data update. We describe the characteristics of the information system designed to act in the life cycle of facilities networks. We focus on the data model design, whose core connects topologically the 3D geometric objects representing facilities components. This scheme enables CRUD (Create, Read, Update, Delete) operations directly on the 3D objects of the digital reality, and through desktop or mobile devices. The system is implemented in a client-server architecture. The server maintains the complete topological data model in a spatial database. This model is redesigned and replicated in the memory of any client device with graphical capabilities. Conventional computers can enable Virtual Reality, while mobile devices can access ubiquitously surrounding facilities through Augmented Reality. In addition, the design of a third data model allows the exchange of this information using a standard format such as CityGML. Current facilities networks have been used to test the CRUD operations functionality.

The increased use of location-aware devices, such as smartphones, generates a large amount of trajectory data. These data can be useful in several domains, like marketing, path modeling, localization of an epidemic focus, and so on. Nevertheless, since trajectory information contains personal mobility data, improper use or publication of trajectory data can threaten users’ privacy. It may reveal sensitive details like habits of behavior, religious beliefs, and sexual preferences. Therefore, many users might be unwilling to share their trajectory data without a previous anonymization process. Currently, several proposals to address this problem can be found in the literature. These solutions focus on anonymizing data before its publication, i.e., when they are already stored in the server database. Nevertheless, we argue that this approach gives the user no control about the information she shares. For this reason, we propose anonymizing data in the users’ mobile devices, before they are sent to a third party. This article extends our previous work which was, to the best of our knowledge, the first one to anonymize data at the client side, allowing users to select the amount and accuracy of shared data. In this article, we describe an improved version of the protocol, and we include the implementation together with an analysis of the results obtained after the simulation with real trajectory data.

Federated Learning (FL) is a popular distributed machine learning paradigm that enables devices to work together to train a centralized model without transmitting raw data. However, when the model becomes complex, mobile devices’ communication overhead can be unacceptably large in traditional FL methods. To address this problem, Federated Distillation (FD) is proposed as a federated version of knowledge distillation. Most of the recent FD methods calculate the model output (logits) of each client as the local knowledge on a public proxy dataset and do distillation with the average of the clients’ logits on the server side. Nevertheless, these FD methods are not robust and perform poorly in the non-IID (data is nonindependent and non-identically distributed) scenario such as Federated Recommendation (FR). In order to eliminate the non-IID problem and apply FD in FR, we proposed a novel method named FedDyn to construct a proxy dataset and extract local knowledge dynamically in this paper. In this method, we replaced the average strategy with focus distillation to strengthen reliable knowledge, which solved the non-IID problem that the local model has biased knowledge. The average strategy is a dilution and perturbation of knowledge since it treats reliable and unreliable knowledge equally important. In addition, to prevent inference of private user information from local knowledge, we used a method like local differential privacy techniques to protect this knowledge on the client side. The experimental results showed that our method has a faster convergence speed and lower communication overhead than the baselines on three datasets, including MovieLens-10OK, MovieLens-IM and Pinterest.

A service capable of supporting human memory requires complex computational tasks that are still unavailable in mobile devices; however, broadband connectivity allows weaving together server side computational power with client side rich content delivery. In this paper, we provide a complete description of the 4W (what-where-when-who) project and its current prototype. The system aims at providing a service that mitigates the negative effects of stress or aging on the memorisation capability of humans leveraging both client side and server side state of the art solutions. The prototype is capable of capturing user needs/to-dos, reasoning to identify where those needs/tasks can be fulfilled/performed efficiently and providing timely and localised hints about the identified sweet-spots. We have tested with real users both the general efficacy of the system and the different level of efficiency of several hint-notification modes: we present our results and we provide a discussion of our findings.

New types of complex mobile devices can operate full scale applications with same comfort as their desktop equivalents only with several limitations. One of them is insufficient transfer speed on wireless connectivity in case of downloading the large multimedia files. Main area of paper is in a model of a system enhancement for locating and tracking users of a mobile information system. User location is used for data prebuffering and pushing information from server to user’s PDA. All server data is saved as artifacts along with its position information in building or larger area environment. The new partial prebuffering techniques is designed and pretested. The accessing of prebuffered data on mobile device can highly improve response time needed to view large multimedia data. This fact can help with design of new full scale applications for mobile devices.

Nowadays processors working in personal computers and mobile devices allow for more and more effective parallel computing. Developers have at their disposal many different methods of implementing concurrency, but usually use the one, that they now best. It is beneficial to know, when a particular technique is good and when it is better to find an alternative. This paper presents different ways of implementing parallel mathematical calculations using threads, tasks, thread pool, task pool and parallel for loop. Each method was used in a C# application running on Windows Presentation Foundation engine on .NET platform. Implemented operation is calculation value of Pi using Leibnitz’s formula.

With reference to the increasing data transfer such as video, music, large files on mobile devices e.g. Smartphones, ipads and electronic tablets, there has been corresponding increment in data traffic in the cellular network as the number of users keeps escalating day by day. In the meantime, mobile internet is rapidly evolving towards embedded internet, expanding its reach from people to machines [1]. In fact, the wireless industry now expects 50 billion machine-type devices connected to the global network by 2020. This has brought a huge burden on cellular network operators. The most economical ways are rerouting the data through complementary technologies such as the use of Wi-Fi, Femto, Heterogeneous, opportunistic networks and so on. It is also noted that each of these networks has its pros and cons. These assist network operators to choose one or more systems over the other. This paper considers the challenges of these offloading networks in Wi-Fi and Femto networks and their coexistence issues with each other and other networks.