A Customizable k-Anonymity Model for Protecting Location Privacy

Continued advances in mobile networks and positioning technologies have created a strong market push for location-based services (LBSs). Examples include location-aware emergency services, location based service advertisement, and location sensitive billing. One of the big challenges in wide deployment of LBS systems is the privacy-preserving management of location-based data. Without safeguards, extensive deployment of location based services endangers location privacy of mobile users and exhibits significant vulnerabilities for abuse. In this paper, we describe a customizable k-anonymity model for protecting privacy of location data. Our model has two unique features. First, we provide a customizable framework to support k-anonymity with variable k, allowing a wide range of users to benefit from the location privacy protection with personalized privacy requirements. Second, we design and develop a novel spatio-temporal cloaking algorithm, called CliqueCloak, which provides location k-anonymity for mobile users of a LBS provider. The cloaking algorithm is run by the location protection broker on a trusted server, which anonymizes messages from the mobile nodes by cloaking the location information contained in the messages to reduce or avoid privacy threats before forwarding them to the LBS provider(s). Our model enables each message sent from a mobile node to specify the desired level of anonymity as well as the maximum temporal and spatial tolerances for maintaining the required anonymity. We study the effectiveness of the cloaking algorithm under various conditions using realistic location data synthetically generated using real road maps and traffic volume data. Our experiments show that the location k-anonymity model with multi-dimensional cloaking and tunable k parameter can achieve high guarantee of k anonymity and high resilience to location privacy threats without significant performance penalty

PeerCQ: A Scalable and Self-Configurable Peer-to-Peer Information Monitoring System

PeerCQ is a peer-to-peer Continual Query system for information monitoring on the Internet. It uses Continual Queries (CQs) as its primitives to express information-monitoring requests. A primary objective of the PeerCQ system is to build a decentralized Internet scale distributed information-monitoring system, which is highly scalable, self-configurable and supports efficient and robust way of processing CQs. In this paper we describe the basic architecture of the PeerCQ system and focus on the mechanisms used for service partitioning and service lookup. There are two unique characteristics of PeerCQ. First, it introduces a donation based peer-aware mechanism for handling the peer heterogeneity. Second, it integrates CQ-aware and peer-aware information into its service partitioning scheme, while maintaining decentralization and self-configurability. We report a set of initial experiments demonstrating the sensitiveness of our approach to peer heterogeneity and the effectiveness of our service partitioning algorithm with respect to load balancing and system utilization

Topic-based influence computation in social networks under resource constraints

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80% higher accuracy for network fetching and 77% for text fetching) and are superior to state-of-the-art techniques from the literature (21% higher accuracy)

Askin's Tumor in an Adult: Case Report and Findings on 18F-FDG PET/CT

Primitive neuroectodermal tumor (PNET) of the chest wall or Askin's tumor is a rare neoplasm of chest wall. It most often affects children and adolescents and is a very rare tumor in adults. In this case report, we present an Askin's tumor occurred in a 73-year-old male. The patient was admitted with a history of 3-month lower back pain and cough. In computed tomography, there was a lesion with dimensions of 70 × 40 × 65 mm in the superior segment of the lower lobe of the left lung. Positron emission tomography/computed tomography with 18F-flourodeoxyglucose revealed a pleural-based tumor in the left lung with a maximum standardized uptake value of 4.36. No distant or lymph node metastases were present. The patient had gone through surgery, and wedge resection of the superior segment of left lobe and partial resection of the ipsilateral ribs were performed. Pathology report with immunocytochemistry was consistent with PNET and the patient received chemotherapy after that

Europeanization without substance? EU-Turkey relations and gender equality in employment

This paper focuses on EU-Turkey relations through gender-related employment policy practices. We argue that Turkey is undergoing a process of ‘Europeanization without substance’, in which vague commitments and policy initiatives to enhance female labour force participation coexist uneasily with a contravening political discourse. This is not merely the result of a stalemate in accession negotiations, nor does it stem from the diversity of employment practices across the Union. It rather results from the deliberative discourses used by Turkey’s political leadership to selectively appropriate certain aspects of Europeanization to further a politically motivated agenda that, in essence, negates gender equality altogether. This, we argue in turn, is reflected in a set of practices, policy initiatives, and public statements that make substantive progress in EU-Turkey relations harder. This process is facilitated by the diminishing emphasis placed by the EU on gender equality in employment as manifested by the evolution of gender equality practices at EU level and reinforced by austerity-led policies during the economic crisis

Scaling Continuous Query Services for Future Computing Platforms and Applications

The ever increasing rate of digital information available from on-line sources drives the need for building information monitoring applications to assist users in tracking relevant changes in these sources and accessing information that is of interest to them in a timely manner. Continuous queries (CQs) are standing queries that are continuously evaluated over dynamic sources to track information changes that meet user specified thresholds and notify users of new results in real-time. CQ systems can be considered as powerful middleware for supporting information monitoring applications. A significant challenge in building CQ systems is scalability, caused by the large number of users and queries, and by the large and growing number of information sources with high update rates. In this thesis we use CQs to shepherd through and address the challenges involved in supporting information monitoring applications in future computing platforms. The focus is on P2P web monitoring in Internet systems, location monitoring in mobile systems, and environmental monitoring in sensor systems. Although different computing platforms require different software architectures for building scalable CQ services, there is a common design philosophy that this thesis advocates for making CQ services scalable and efficient. This can be summarized as "move computation close to the places where the data is produced." A common challenge in scaling CQ systems is the resource-intensive nature of query evaluation, which involves continuously checking updates in a large number of data sources and evaluating trigger conditions of a large number of queries over these updates, consuming both cpu and network bandwidth resources. If some part of the query evaluation can be pushed close to the sources where the data is produced, the resulting early filtering of updates will save both bandwidth and cpu resources. In summary, in this thesis we show that distributed CQ architectures that are designed to take advantage of the opportunities provided by ubiquitous computing platforms and pervasive networks, while at the same time recognizing and resolving the challenges posed by these platforms, lead to building scalable and effective CQ systems to better support the demanding information monitoring applications of the future.Ph.D.Committee Chair: Ling Liu; Committee Member: Calton Pu; Committee Member: Christian S. Jensen; Committee Member: Kishore Ramachandran; Committee Member: Leo Mark; Committee Member: Sol M. Shat