Xml-Based Privacy Model in Pervasive Computing

The years coming promise to bring new area of information technology, transferring it from scientists minds into reality, on one hand a new paradigm known as pervasive calm, ubiquitous computing, or pervasive computing has the ability to overcome a lot of insufficiencies of the current information systems while on the other hand central blocks of pervasive computing are in direct conflicts with privacy protection fundamentals. Considerable efforts have been taken to cope with this problem but each one had its own shortage. Some just provide one privacy type like location privacy or just identity privacy, some of them were not platform independence, and some resulted to a lot of privacy alarms. In this thesis we proposed a new privacy model in pervasive computing that provides all four privacy types (ID, Location, Time, and content) for the user with high control over private information (User Control over Private Information) and as less privacy warnings as possible (Unobtrusiveness of Privacy Mechanism). To complete the proposed model we showed model privacy policies with XML tags and distributed decision making processes in different layers to provide high scalability. To validate the model, through implementation we showed that model provides “Privacy Policy Expressiveness” with supporting mandatory and discretionary rules, uncertainty handling and conflict resolution. We showed model unobtrusiveness with experimenting and measuring the time user wastes on dealing with privacy sub-system. We showed that our model provides content, identity, location and time privacy that leads to a high level of user control over private information. The model scalability would be granted by using XML as a platform independent format to describe privacy policies with addition of distributed decision making processes. The validation results confirmed that the model supports all four metrics of “expressiveness of privacy policies”, all four metrics of “user control over private information”, and both factors of “scalability”, with less than 10% “unobtrusiveness”

A Clustering-based Location Privacy Protection Scheme for Pervasive Computing

In pervasive computing environments, Location- Based Services (LBSs) are becoming increasingly important due to continuous advances in mobile networks and positioning technologies. Nevertheless, the wide deployment of LBSs can jeopardize the location privacy of mobile users. Consequently, providing safeguards for location privacy of mobile users against being attacked is an important research issue. In this paper a new scheme for safeguarding location privacy is proposed. Our approach supports location K-anonymity for a wide range of mobile users with their own desired anonymity levels by clustering. The whole area of all users is divided into clusters recursively in order to get the Minimum Bounding Rectangle (MBR). The exact location information of a user is replaced by his MBR. Privacy analysis shows that our approach can achieve high resilience to location privacy threats and provide more privacy than users expect. Complexity analysis shows clusters can be adjusted in real time as mobile users join or leave. Moreover, the clustering algorithms possess strong robustness.Comment: The 3rd IEEE/ACM Int Conf on Cyber, Physical and Social Computing (CPSCom), IEEE, Hangzhou, China, December 18-20, 201

Privacy in (mobile) telecommunications services

Telecommunications services are for long subject to privacy regulations. At stake are traditionally: privacy of the communication and the protection of traffic data. Privacy of the communication is legally founded. Traffic data subsume under the notion of data protection and are central in the discussion. The telecommunications environment is profoundly changing. The traditionally closed markets with closed networks change into an open market with open networks. Within these open networks more privacy sensitive data are generated and have to be exchanged between growing numbers of parties. Also telecommunications and computer networks are rapidly being integrated and thus the distinction between telephony and computing disappears. Traditional telecommunications privacy regulations are revised to cover internet applications. In this paper telecommunications issues are recalled to aid the on-going debate. Cellular mobile phones have recently be introduced. Cellular networks process a particular category of traffic data namely location data, thereby introducing the issue of territorial privacy into the telecommunications domain. Location data are bound to be used for pervasive future services. Designs for future services are discussed and evaluated for their impact on privacy protection.</p

A XML based, user-centered privacy model in pervasive computing systems

The fact that pervasive systems are typically embedded and invisible makes it difficult for users to know when, where, and how these devices are collecting data. So privacy is a major issue for pervasive computing applications and several privacy models have been proposed for pervasive environments. In this paper we present a XML based User-centered Privacy Model (UPM) which provides content, identity, location, and time privacy with low unobtrusivenes

PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146

A distributed architecture for mobile, location-dependent applications

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 56-58).As pervasive computing becomes a reality, users will be able to interact with computing services which will all work together in a seamlessly integrated system. Resource discovery is a key feature of pervasive computing which allows users simple and convenient access to resources. While there are several resource discovery systems in existence which are essential to pervasive computing, the support for location-based resource discovery is uncommon. The goal of this work is to integrate a location-support system with a resource discovery system to support location-based resource discovery. Our integrated system provides an in-building solution which is cost-effective, scalable, and distributed. In order to protect the user's privacy, the system provides location discovery services without receiving any information from the user. We have also implemented a simple scripting language which thin clients can use to control and interact with network services. To demonstrate the power and functionality of this system, we have deployed several location-dependent applications including a map-based service discovery application with little manual configuration.by Anit Chakraborty.M.Eng

From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern and the subjects do not have to carry a device on them. Recently, it has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments show that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. We are also the first to use complex-valued CSI to improve the performance in the environment with RFI