Bugra Gedik

Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms

Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An important challenge in the wide deployment of location-based services (LBSs) is the privacy-aware management of location information, providing safeguards for location privacy of mobile clients against vulnerabilities for abuse. This paper describes a scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs. This architecture includes the development of a personalized location anonymization model and a suite of location perturbation algorithms. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k-anonymity for a wide range of mobile clients with context-sensitive privacy requirements. This framework enables each mobile client to specify the minimum level of anonymity that it desires and the maximum temporal and spatial tolerances that it is willing to accept when requesting k-anonymity-preserving LBSs. We devise an efficient message perturbation engine to implement the proposed location privacy framework. The prototype that we develop is designed to be run by the anonymity server on a trusted platform and performs location anonymization on LBS request messages of mobile clients such as identity removal and spatio-temporal cloaking of the location information. We study the effectiveness of our location cloaking algorithms under various conditions by using realistic location data that is synthetically generated from real road maps and traffic volume data. Our experiments show that the personalized location k-anonymity model, together with our location perturbation engine, can achieve high resilience to location privacy threats without introducing any significant performance penalty.

MobiEyes: Distributed processing of continuously moving queries on moving objects in a mobile system

Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very few have promoted a distributed approach to real-time location monitoring. In this paper we present a distributed and scalable solution to processing continuously moving queries on moving objects and describe the design of MobiEyes, a distributed real-time location monitoring system in a mobile environment. Mobieyes utilizes the computational power at mobile objects, leading to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information at the server. We introduce a set of optimization techniques, such as Lazy Query Propagation, Query Grouping, and Safe Periods, to constrict the amount of computations handled by the moving objects and to enhance the performance and system utilization of Mobieyes. We also provide a simulation model in a mobile setup to study the scalability of the MobiEyes distributed location monitoring approach with regard to server load, messaging cost, and amount of computation required on the mobile objects.

ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks

One of the most prominent and comprehensive ways of data collection in sensor networks is to periodically extract raw sensor readings. This way of data collection enables complex analysis of data, which may not be possible with in-network aggregation or query processing. However, this flexibility in data analysis comes at the cost of power consumption. In this paper, we develop ASAP, which is an adaptive sampling approach to energy-efficient periodic data collection in sensor networks. The main idea behind ASAP is to use a dynamically changing subset of the nodes as samplers such that the sensor readings of the sampler nodes are directly collected, whereas the values of the nonsampler nodes are predicted through the use of probabilistic models that are locally and periodically constructed. ASAP can be effectively used to increase the network lifetime while keeping the quality of the collected data high in scenarios where either the spatial density of the network deployment is superfluous, which is relative to the required spatial resolution for data analysis, or certain amount of data quality can be traded off in order to decrease the power consumption of the network. The ASAP approach consists of three main mechanisms: First, sensing-driven cluster construction is used to create clusters within the network such that nodes with close sensor readings are assigned to the same clusters. Second, correlation-based sampler selection and model derivation are used to determine the sampler nodes and to calculate the parameters of the probabilistic models that capture the spatial and temporal correlations among the sensor readings. Last, adaptive data collection and model-based prediction are used to minimize the number of messages used to extract data from the network. A unique feature of ASAP is the use of in-network schemes, as opposed to the protocols requiring centralized control, to select and dynamically refine the subset of the sensor nodes serving as samplers and to adjust the value prediction models used for nonsampler nodes. Such runtime adaptations create a data collection schedule, which is self-optimizing in response to the changes in the energy levels of the nodes and environmental dynamics. We present simulation-based experimental results and study the effectiveness of ASAP under different system settings.

A catalog of stream processing optimizations

Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, and complex event processing. Since each community faces applications with challenging performance requirements, each of them has developed some of the same optimizations, but often with conflicting terminology and unstated assumptions. This article presents a survey of optimizations for stream processing. It is aimed both at users who need to understand and guide the system’s optimizer and at implementers who need to make engineering tradeoffs. To consolidate terminology, this article is organized as a catalog, in a style similar to catalogs of design patterns or refactorings. To make assumptions explicit and help understand tradeoffs, each optimization is presented with its safety constraints (when does it preserve correctness?) and a profitability experiment (when does it improve performance?). We hope that this survey will help future streaming system builders to stand on the shoulders of giants from not just their own community.

Elastic scaling of data parallel operators in stream processing

We describe an approach to elastically scale the performance of a data analytics operator that is part of a streaming application. Our techniques focus on dynamically adjusting the amount of computation an operator can carry out in response to changes in incoming workload and the availability of processing cycles. We show that our elastic approach is beneficial in light of the dynamic aspects of streaming workloads and stream processing environments. Addressing another recent trend, we show the importance of our approach as a means to providing computational elasticity in multicore processor-based environments such that operators can automatically find their best operating point. Finally, we present experiments driven by synthetic workloads, showing the space where the optimizing efforts are most beneficial and a radioastronomy imaging application, where we observe substantial improvements in its performance-critical section.

Elastic Scaling for Data Stream Processing

This article addresses the profitability problem associated with auto-parallelization of general-purpose distributed data stream processing applications. Auto-parallelization involves locating regions in the applications data flow graph that can be replicated at run-time to apply data partitioning, in order to achieve scale. In order to make auto-parallelization effective in practice, the profitability question needs to be answered: How many parallel channels provide the best throughput? The answer to this question changes depending on the workload dynamics and resource availability at run-time. In this article, we propose an elastic auto-parallelization solution that can dynamically adjust the number of channels used to achieve high throughput without unnecessarily wasting resources. Most importantly, our solution can handle partitioned stateful operators via run-time state migration, which is fully transparent to the application developers. We provide an implementation and evaluation of the system on an industrial-strength data stream processing platform to validate our solution.

A distributed approach to node clustering in decentralized peer-to-peer networks

Connectivity-based node clustering has wide-ranging applications in decentralized peer-to-peer (P2P) networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks, and so forth. This paper describes a connectivity-based distributed node clustering scheme (CDC). This scheme presents a scalable and efficient solution for discovering connectivity-based clusters in peer networks. In contrast to centralized graph clustering algorithms, the CDC scheme is completely decentralized and it only assumes the knowledge of neighbor nodes instead of requiring a global knowledge of the network (graph) to be available. An important feature of the CDC scheme is its ability to cluster the entire network automatically or to discover clusters around a given set of nodes. To cope with the typical dynamics of P2P networks, we provide mechanisms to allow new nodes to be incorporated into appropriate existing clusters and to gracefully handle the departure of nodes in the clusters. These mechanisms enable the CDC scheme to be extensible and adaptable in the sense that the clustering structure of the network adjusts automatically as nodes join or leave the system. We provide detailed experimental evaluations of the CDC scheme, addressing its effectiveness in discovering good quality clusters and handling the node dynamics. We further study the types of topologies that can benefit best from the connectivity-based distributed clustering algorithms like CDC. Our experiments show that utilizing message-based connectivity structure can considerably reduce the messaging cost and provide better utilization of resources, which in turn improves the quality of service of the applications executing over decentralized peer-to-peer networks.

PINCO: a pipelined in-network compression scheme for data collection in wireless sensor networks

In this paper, we present PINCO, an in-network compression scheme for energy constrained, distributed, wireless sensor networks. PINCO reduces redundancy in the data collected from sensors, thereby decreasing the wireless communication among the sensor nodes and saving energy. Sensor data is buffered in the network and combined through a pipelined compression scheme into groups of data, while satisfying a user-specified end-to-end latency bound. We introduce a PINCO scheme for single-valued sensor readings. In this scheme, each group of data is a highly flexible structure so that compressed data can be recompressed without decompressing, in order to reduce newly available redundancy at a different stage of the network. We discuss how PINCO parameters affect its performance, and how to tweak them for different performance requirements. We also include a performance study demonstrating the advantages of our approach over other data collection schemes based on simulation and prototype deployment results.

PeerCQ: a decentralized and self-configuring peer-to-peer information monitoring system

PeerCQ is a totally decentralized system that performs information monitoring tasks over a network of peers with heterogeneous capabilities. 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. This paper describes the basic architecture of the PeerCQ system and focuses on the mechanisms used for service partitioning at the P2P protocol layer. A set of initial experiments is reported, demonstrating the sensitiveness of the PeerCQ approach to large scale P2P information monitoring and the effectiveness of the PeerCQ service-partitioning algorithms with respect to load balancing and system utilization.

Streaming algorithms for k-core decomposition

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-Hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for streaming graph data. In this paper, we propose the first incremental k-core decomposition algorithms for streaming graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have to be updated, and efficiently process this subgraph to update the k-core decomposition. Our results show a significant reduction in run-time compared to non-incremental alternatives. We show the efficiency of our algorithms on different types of real and synthetic graphs, at different scales. For a graph of 16 million vertices, we observe speedups reaching a million times, relative to the non-incremental algorithms.