Location privacy in wireless personal area networks

Location privacy is one of the major security problems in a Wireless Personal Area Network (WPAN). By eavesdropping on the transmitted packets, an attacker can keep track of the place and time of the communication between the mobile devices. The hardware address of the device can often be linked to the identity of the user operating the mobile device; this represents a violation of the user's privacy. Fortunately, this problem can be solved quite efficiently in a WPAN. We consider four communication scenarios and present several techniques to solve the location privacy problem in each of these scenarios. As mobile devices in a WPAN are typically operated by a user and energy constrained, we focused on user-friendliness and energy consumption during the design of our solutions. Copyright 2006 ACM.status: publishe

De-anonymizable location cloaking for privacy-controlled mobile systems

The rapid technology upgrades of mobile devices and the popularity of wireless networks significantly drive the emergence and development of Location-based Services (LBSs), thus greatly expanding the business of online services and enriching the user experience. However, the personal location data shared with the service providers also leave hidden risks on location privacy. Location anonymization techniques transform the exact location of a user into a cloaking area by including the locations of multiple users in the exposed area such that the exposed location is indistinguishable from that of the other users. However in such schemes, location information once perturbed cannot be recovered from the cloaking region and as a result, users of the location cannot obtain fine granular information even when they have access to it. In this paper, we propose Dynamic Reversible Cloaking (DRC) a new de-anonymziable location cloaking mechanism that allows to restore the actual location from the perturbed information through the use of an anonymization key. Extensive experiments using realistic road network traces show that the proposed scheme is efficient, effective and scalable

Localization to Enhance Security and Services in Wi-Fi Networks under Privacy Constraints

Developments of seamless mobile services are faced with two broad challenges, systems security and user privacy - access to wireless systems is highly insecure due to the lack of physical boundaries and, secondly, location based services (LBS) could be used to extract highly sensitive user information. In this paper, we describe our work on developing systems which exploit location information to enhance security and services under privacy constraints. We describe two complimentary methods which we have developed to track node location information within production University Campus Networks comprising of large numbers of users. The location data is used to enhance security and services. Specifically, we describe a method for creating geographic firewalls which allows us to restrict and enhance services to individual users within a specific containment area regardless of physical association. We also report our work on LBS development to provide visualization of spatio-temporal node distribution under privacy considerations

Securing personal distributed environments

The Personal Distributed Environment (PDE) is a new concept being developed by Mobile VCE allowing future mobile users flexible access to their information and services. Unlike traditional mobile communications, the PDE user no longer needs to establish his or her personal communication link solely through one subscribing network but rather a diversity of disparate devices and access technologies whenever and wherever he or she requires. Depending on the services’ availability and coverage in the location, the PDE communication configuration could be, for instance, via a mobile radio system and a wireless ad hoc network or a digital broadcast system and a fixed telephone network. This new form of communication configuration inherently imposes newer and higher security challenges relating to identity and authorising issues especially when the number of involved entities, accessible network nodes and service providers, builds up. These also include the issue of how the subscribed service and the user’s personal information can be securely and seamlessly handed over via multiple networks, all of which can be changing dynamically. Without such security, users and operators will not be prepared to trust their information to other networks

A Middleware for the Internet of Things

The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as things to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things, or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.2, March 201

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions