Liu, Ling

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Now showing 1 - 10 of 46
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    ITR/SI: Guarding the next internet frontier: countering denial of information
    (Georgia Institute of Technology, 2008-12-19) Ahamad, Mustaque ; Omiecinski, Edward ; Pu, Calton ; Mark, Leo ; Liu, Ling ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. Office of Sponsored Programs
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    A Hybrid Access Model for Storage Area Networks
    (Georgia Institute of Technology, 2004) Singh, Aameek ; Voruganti, Kaladhar ; Gopisetty, Sandeep ; Pease, David ; Liu, Ling
    We present HSAN - a hybrid storage area network, which uses both in-band (like NFS) and out-of-band virtualization (like SAN FS) access models. Using hybrid servers that can serve as both metadata and NAS servers, HSAN intelligently decides the access model per each request, based on the characteristics of requested data. This hybrid model is implemented using low overhead cache-admission and cache-replacement schemes and aims to improve overall response times for a wide variety of workloads. Preliminary analysis of the hybrid model indicates performance improvements over both models.
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    PRIVACYGRID: Supporting Anonymous Location Queries in Mobile Environments
    (Georgia Institute of Technology, 2007) Bamba, Bhuvan ; Liu, Ling
    We present PRIVACYGRID − a framework for supporting anonymous location-based queries in mobile information delivery systems. The PRIVACYGRID framework offers three unique capabilities. First, we provide a location privacy preference profile model, called location P3P, which allows mobile users to explicitly define their preferred location privacy requirements in terms of both location hiding measures (e.g., location k-anonymity and location l-diversity) and location service quality measures (e.g., maximum spatial resolution and maximum temporal resolution). Second, we develop three fast and effective location cloaking algorithms for providing location k-anonymity and location l-diversity in a mobile environment. The Quad Grid cloaking algorithm is fast but has lower anonymization success rate. The dynamic bottom-up or top-down grid cloaking algorithms provide much higher anonymization success rate and yet are efficient in terms of both time complexity and maintenance cost. Finally, we discuss a hybrid approach that combines the topdown and bottom-up search of location cloaking regions to further lower the average anonymization time. In addition, we argue for incorporating temporal cloaking into the location cloaking process to further increase the success rate of location anonymization. We also discuss the PRIVACYGRID mechanisms for anonymous support of range queries. Our experimental evaluation shows that the PRIVACYGRID approach can provide optimal location anonymity as defined by per user location P3P without introducing significant performance penalties.
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    Write-Optimized Indexing for Log-Structured Key-Value Stores
    (Georgia Institute of Technology, 2014) Tang, Yuzhe ; Iyengar, Arun ; Tan, Wei ; Fong, Liana ; Liu, Ling ; Georgia Institute of Technology. Center for Experimental Research in Computer Systems ; Georgia Institute of Technology. College of Computing ; IBM Thomas J. Watson Research Center
    The recent shift towards write-intensive workload on big data (e.g., financial trading, social user-generated data streams) has pushed the proliferation of the log-structured key-value stores, represented by Google’s BigTable, HBase and Cassandra; these systems optimize write performance by adopting a log-structured merge design. While providing key-based access methods based on a Put/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in many web and Internet applications, such as real-time search for all tweets or blogs containing “government shutdown”. In this paper, we present HINDEX, a write-optimized indexing scheme on the log-structured key-value stores. To index intensively updated big data in real time, the index maintenance is made lightweight by a design tailored to the unique characteristic of the underlying log-structured key-value stores. Concretely, HINDEX performs append-only index updates, which avoids the reading of historic data versions, an expensive operation in the log-structure store. To fix the potentially obsolete index entries, HINDEX proposes an offline index repair process through tight coupling with the routine compactions. HINDEX’s system design is generic to the Put/Get interface; we implemented a prototype of HINDEX based on HBase without internal code modification. Our experiments show that the HINDEX offers significant performance advantage for the write-intensive index maintenance.
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    A Customizable k-Anonymity Model for Protecting Location Privacy
    (Georgia Institute of Technology, 2004-04-07) Gedik, Bugra ; Liu, Ling
    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.
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    A peer to peer approach to large scale information monitoring
    (Georgia Institute of Technology, 2008-10-31) Liu, Ling ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. Center for Experimental Research in Computer Systems ; Georgia Institute of Technology. Office of Sponsored Programs
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    PeerTrust: A Trust Mechanism for an Open Peer-to-Peer Information System
    (Georgia Institute of Technology, 2002) Xiong, Li ; Liu, Ling
    In an open peer-to-peer information system, peers often have to interact with unknown or unfamiliar peers and need to manage the risk that is involved with the interactions without any presence of trusted third parties or trust authorities. It is important for peers to be able to reason about trust when interacting with each other to accomplish a task. This paper presents PeerTrust, a simple and yet effective trust mechanism for quantifying and comparing the trustworthiness of peers. We argue that the amount of satisfaction a peer obtains through interactions, the total number of interactions that a peer has with other peers, and the balancing factor of trust all play a crucial role in evaluating the trustworthiness of the peer. This paper also discusses the architecture and the design considerations in implementing this mechanism in a decentralized peer-to-peer system. We report the set of initial experiments, showing the feasibility, the cost, and the benefit of our approach.
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    Discovering and Ranking Data Intensive Web Services: A Source-Biased Approach
    (Georgia Institute of Technology, 2003) Caverlee, James ; Liu, Ling ; Rocco, Daniel J. (Daniel John)
    This paper presents a novel source-biased approach to automatically discover and rank relevant data intensive web services. It supports a service-centric view of the Web through source-biased probing and source-biased relevance detection and ranking metrics. Concretely, our approach is capable of answering source-centric queries by focusing on the nature and degree of the topical relevance of one service to others. This source-biased probing allows us to determine in very few interactions whether a target service is relevant to the source by probing the target with very precise probes and then ranking the relevant services discovered based on a set of metrics we define. Our metrics allow us to determine the nature and degree of the relevance of one service to another. We also introduce a performance enhancement to our basic approach called source-biased probing with focal terms. We also extend the basic probing framework to a more generalized service neighborhood graph model. We discuss the semantics of the neighborhood graph, how we may reason about the relationships among multiple services, and how we rank services based on the service neighborhood graph model. We also report initial experiments to show the effectiveness of our approach.
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    GRUBJOIN: An Adaptive Multi-Way Windowed Stream Join with Time Correlation-Aware CPU Load Shedding
    (Georgia Institute of Technology, 2005) Gedik, Bugra ; Wu, Kun-Lung ; Yu, Philip S. ; Liu, Ling
    Dropping tuples has been commonly used for load shedding. However, tuple dropping generally is inadequate to shed load for multiway windowed stream joins. The output rate can be unnecessarily and severely degraded because tuple dropping does not recognize time correlations likely to exist among the streams. This paper introduces GrubJoin: an adaptive multi-way windowed stream join that efficiently performs time correlation-aware CPU load shedding. GrubJoin maximizes the output rate by achieving nearoptimal window harvesting within an operator throttling framework, i.e., regulating the fractions of the join windows that are processed by the multi-way join. Window harvesting performs the join using only certain more useful segments of the join windows. Due mainly to the combinatorial explosion of possible multi-way join sequences involving various segments of individual join windows, GrubJoin faces a set of unique challenges, such as determining the optimal window harvesting configuration and learning the time correlations among the streams. To tackle these challenges, we formalize window harvesting as an optimization problem, develop greedy heuristics to determine near-optimal window harvesting configurations and use approximation techniques to capture the time correlations among the streams. Experimental results show that GrubJoin is vastly superior to tuple dropping when time correlations exist among the streams and is equally effective as tuple dropping in the absence of time correlations.
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    Mondrian Tree: Efficient Indexing Structure for Scalable Spatial Triggers Processing over Mobile Environment
    (Georgia Institute of Technology, 2010) Doo, Myungcheol ; Liu, Ling ; Narasimhan, Nitya ; Vasudevan, Venu ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. Center for Experimental Research in Computer Systems ; Georgia Institute of Technology. School of Computer Science ; Motorola, inc. Applied Research Center
    Spatial Alarms are reminders for mobile users upon their arrival of certain spatial location of interest. Spatial alarm processing requires meeting two demanding objectives: high accuracy, which ensures zero or very low alarm misses, and high scalability, which requires highly efficient and optimal processing of spatial alarms. Existing techniques for processing spatial alarms cannot solve these two problems at the same time. In this paper we present the design and implementation of a new indexing technique, Mondrian tree. The Mondrian tree indexing method partitions the entire universe of discourse into spatial alarm monitoring regions and alarm-free regions. This enables us to reduce the number of on-demand alarm-free region computations, significant saving of both server load and client to server communication cost. We evaluate the efficiency of the Mondrian tree indexing approach using a road network simulator and show that the Mondrian tree offers significant performance enhancements on spatial alarm processing at both the server side and the client side.