Bugra Gedik

Bio: Bugra Gedik is an academic researcher from Bilkent University. The author has contributed to research in topics: Stream processing & Scalability. The author has an hindex of 39, co-authored 163 publications receiving 6805 citations. Previous affiliations of Bugra Gedik include IBM & Georgia Institute of Technology College of Computing.

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

Abstract: 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.

Location Privacy in Mobile Systems: A Personalized Anonymization Model

Abstract: This paper describes a personalized k-anonymity model for protecting location privacy against various privacy threats through location information sharing. Our model has two unique features. First, we provide a unified privacy personalization framework to support location k-anonymity for a wide range of users with context-sensitive personalized privacy requirements. This framework enables each mobile node to specify the minimum level of anonymity it desires as well as the maximum temporal and spatial resolutions it is willing to tolerate when requesting for k-anonymity preserving location-based services (LBSs). Second, we devise an efficient message perturbation engine which runs by the location protection broker on a trusted server and performs location anonymization on mobile users' LBS request messages, such as identity removal and spatio-temporal cloaking of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloak algorithms, to provide high quality personalized location k-anonymity, aiming at avoiding or reducing known location privacy threats before forwarding requests to LBS provider(s). The effectiveness of our CliqueCloak algorithms is studied under various conditions using realistic location data synthetically generated using real road maps and traffic volume data

SPADE: the system s declarative stream processing engine

Abstract: In this paper, we present Spade - the System S declarative stream processing engine. System S is a large-scale, distributed data stream processing middleware under development at IBM T. J. Watson Research Center. As a front-end for rapid application development for System S, Spade provides (1) an intermediate language for flexible composition of parallel and distributed data-flow graphs, (2) a toolkit of type-generic, built-in stream processing operators, that support scalar as well as vectorized processing and can seamlessly inter-operate with user-defined operators, and (3) a rich set of stream adapters to ingest/publish data from/to outside sources. More importantly, Spade automatically brings performance optimization and scalability to System S applications. To that end, Spade employs a code generation framework to create highly-optimized applications that run natively on the Stream Processing Core (SPC), the execution and communication substrate of System S, and take full advantage of other System S services. Spade allows developers to construct their applications with fine granular stream operators without worrying about the performance implications that might exist, even in a distributed system. Spade's optimizing compiler automatically maps applications into appropriately sized execution units in order to minimize communication overhead, while at the same time exploiting available parallelism. By virtue of the scalability of the System S runtime and Spade's effective code generation and optimization, we can scale applications to a large number of nodes. Currently, we can run Spade jobs on ≈ 500 processors within more than 100 physical nodes in a tightly connected cluster environment. Spade has been in use at IBM Research to create real-world streaming applications, ranging from monitoring financial market feeds to radio telescopes to semiconductor fabrication lines.

A catalog of stream processing optimizations

Abstract: 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 correctnessq) and a profitability experiment (when does it improve performanceq). 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 for Data Stream Processing

Abstract: 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 application's 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.

Energy conservation in wireless sensor networks: A survey

Abstract: In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.