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

Sharing Computer Network Logs for Security and Privacy: A Motivation for New Methodologies of Anonymization

Logs are one of the most fundamental resources to any security professional. It is widely recognized by the government and industry that it is both beneficial and desirable to share logs for the purpose of security research. However, the sharing is not happening or not to the degree or magnitude that is desired. Organizations are reluctant to share logs because of the risk of exposing sensitive information to potential attackers. We believe this reluctance remains high because current anonymization techniques are weak and one-size-fits-all--or better put, one size tries to fit all. We must develop standards and make anonymization available at varying levels, striking a balance between privacy and utility. Organizations have different needs and trust other organizations to different degrees. They must be able to map multiple anonymization levels with defined risks to the trust levels they share with (would-be) receivers. It is not until there are industry standards for multiple levels of anonymization that we will be able to move forward and achieve the goal of widespread sharing of logs for security researchers.Comment: 17 pages, 1 figur

Secure and Authenticated Message Dissemination in Vehicular ad hoc Networks and an Incentive-Based Architecture for Vehicular Cloud

Vehicular ad hoc Networks (VANETs) allow vehicles to form a self-organized network. VANETs are likely to be widely deployed in the future, given the interest shown by industry in self-driving cars and satisfying their customers various interests. Problems related to Mobile ad hoc Networks (MANETs) such as routing, security, etc.have been extensively studied. Even though VANETs are special type of MANETs, solutions proposed for MANETs cannot be directly applied to VANETs because all problems related to MANETs have been studied for small networks. Moreover, in MANETs, nodes can move randomly. On the other hand, movement of nodes in VANETs are constrained to roads and the number of nodes in VANETs is large and covers typically large area. The following are the contributions of the thesis. Secure, authenticated, privacy preserving message dissemination in VANETs: When vehicles in VANET observe phenomena such as accidents, icy road condition, etc., they need to disseminate this information to vehicles in appropriate areas so the drivers of those vehicles can take appropriate action. When such messages are disseminated, the authenticity of the vehicles disseminating such messages should be verified while at the same time the anonymity of the vehicles should be preserved. Moreover, to punish the vehicles spreading malicious messages, authorities should be able to trace such messages to their senders when necessary. For this, we present an efficient protocol for the dissemination of authenticated messages. Incentive-based architecture for vehicular cloud: Due to the advantages such as exibility and availability, interest in cloud computing has gained lot of attention in recent years. Allowing vehicles in VANETs to store the collected information in the cloud would facilitate other vehicles to retrieve this information when they need. In this thesis, we present a secure incentive-based architecture for vehicular cloud. Our architecture allows vehicles to collect and store information in the cloud; it also provides a mechanism for rewarding vehicles that contributing to the cloud. Privacy preserving message dissemination in VANETs: Sometimes, it is sufficient to ensure the anonymity of the vehicles disseminating messages in VANETs. We present a privacy preserving message dissemination protocol for VANETs

Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs

Location privacy is a critical problem in the vehicular communication networks. Vehicles broadcast their road status information to other entities in the network through beacon messages to inform other entities in the network. The beacon message content consists of the vehicle ID, speed, direction, position, and other information. An adversary could use vehicle identity and positioning information to determine vehicle driver behavior and identity at different visited location spots. A pseudonym can be used instead of the vehicle ID to help in the vehicle location privacy. These pseudonyms should be changed in appropriate way to produce uncertainty for any adversary attempting to identify a vehicle at different locations. In the existing research literature, pseudonyms are changed during silent mode between neighbors. However, the use of a short silent period and the visibility of pseudonyms of direct neighbors provides a mechanism for an adversary to determine the identity of a target vehicle at specific locations. Moreover, privacy is provided to the driver, only within the RSU range; outside it, there is no privacy protection. In this research, we address the problem of location privacy in a highway scenario, where vehicles are traveling at high speeds with diverse traffic density. We propose a Dynamic Grouping and Virtual Pseudonym-Changing (DGVP) scheme for vehicle location privacy. Dynamic groups are formed based on similar status vehicles and cooperatively change pseudonyms. In the case of low traffic density, we use a virtual pseudonym update process. We formally present the model and specify the scheme through High-Level Petri Nets (HLPN). The simulation results indicate that the proposed method improves the anonymity set size and entropy, provides lower traceability, reduces impact on vehicular network applications, and has lower computation cost compared to existing research work

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice