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Liu, Ling

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Start date: 2010 × End date: 2014 × Author: Tang, Yuzhe ×

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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
    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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    𝜖-PPI: Searching Information Networks with Quantitative Privacy Guarantee
    (Georgia Institute of Technology, 2014) Tang, Yuzhe ; Liu, Ling ; Iyengar, Arun
    In information sharing networks, having a privacy preserving index (or PPI) is critically important for providing efficient search on access controlled content across distributed providers while preserving privacy. An understudied problem for PPI techniques is how to provide controllable privacy preservation, given the innate difference of privacy of the different content and providers. In this paper we present a configurable privacy preserving index, coined 𝜖-PPI, which allows for quantitative privacy protection levels on fine-grained data units. We devise a new common-identity attack that breaks existing PPI’s and propose an identity-mixing protocol against the attack in 𝜖-PPI. The proposed 𝜖-PPI construction protocol is the first without any trusted third party and/or trust relationship between providers. We have implemented our 𝜖-PPI construction protocol by using generic MPC techniques (secure multiparty computation) and optimized the performance to a practical level by minimizing the costly MPC computation part.
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    Authentication of Freshness for OutsourcedMulti-Version Key-Value Stores
    (Georgia Institute of Technology, 2014) Tang, Yuzhe ; Wang, Ting ; Hu, Xin ; Jang, Jiyong ; Liu, Ling ; Pietzuch, Peter
    Data outsourcing offers cost-effective computing power to manage massive data streams and reliable access to data. For example, data owners can forward their data to clouds, and the clouds provide data mirroring, backup, and online access services to end users. However, outsourcing data to untrusted clouds requires data authentication and query integrity to remain in the control of the data owners and users. In this paper, we address this problem specifically for multiversion key-value data that is subject to continuous updates under the constraints of data integrity, data authenticity, and “freshness” (i.e., ensuring that the value returned for a key is the latest version).We detail this problem and propose INCBMTREE, a novel construct delivering freshness and authenticity. Compared to existing work, we provide a solution that offers (i) lightweight signing and verification on massive data update streams for data owners and users (e.g., allowing for small memory footprint and CPU usage on mobile user devices), (ii) integrity of both real-time and historic data, and (iii) support for both real-time and periodic data publication. Extensive benchmark evaluations demonstrate that INCBMTREE achieves more throughput (in an order of magnitude) for data stream authentication than existing work. For data owners and end users that have limited computing power, INCBM-TREE can be a practical solution to authenticate the freshness of outsourced data while reaping the benefits of broadly available cloud services.