On the fly gaussian elimination for LT codes

Bioglio, Valerio; Grangetto, Marco; Gaeta, Rossano; Sereno, Matteo

doi:10.1109/lcomm.2009.12.091824

Cited by 76 publications

(61 citation statements)

References 4 publications

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“…Upon receiving this request, each SN sends to the Proxy the x coded fragments of S it stores. The Proxy then progressively decodes the original sector S using the On-the-Fly Gaussian Elimination (OFG) [20], [21] algorithm. Any set of k ′ ≥ k coded fragments can be interpreted as a linear system of equations that can be solved with Gaussian Elimination to get the original k sector fragment (provided that k independent equations are available).…”

Section: System Modelmentioning

confidence: 99%

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Securing Coding-Based Cloud Storage Against Pollution Attacks

Anglano

Gaeta

Grangetto

2017

IEEE Trans. Parallel Distrib. Syst.

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Abstract-The widespread diffusion of distributed and cloud storage solutions has changed dramatically the way users, system designers, and service providers manage their data. Outsourcing data on remote storage provides indeed many advantages in terms of both capital and operational costs. The security of data outsourced to the cloud, however, still represents one of the major concerns for all stakeholders. Pollution attacks, whereby a set of malicious entities attempt to corrupt stored data, are one of the many risks that affect cloud data security. In this paper we deal with pollution attacks in coding-based block-level cloud storage systems, i.e. systems that use linear codes to fragment, encode, and disperse virtual disk sectors across a set of storage nodes to achieve desired levels of redundancy, and to improve reliability and availability without sacrificing performance. Unfortunately, the effects of a pollution attack on linear coding can be disastrous, since a single polluted fragment can propagate pervasively in the decoding phase, thus hampering the whole sector. In this work we show that, using rateless codes, we can design an early pollution detection algorithm able to spot the presence of an attack while fetching the data from cloud storage during the normal disk reading operations. The alarm triggers a procedure that locates the polluting nodes using the proposed detection mechanism along with statistical inference. The performance of the proposed solution is analyzed under several aspects using both analytical modelling and accurate simulation using real disk traces. Our results show that the proposed approach is very robust and is able to effectively isolate the polluters, even in harsh conditions, provided that enough data redundancy is used.

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Section: System Modelmentioning

confidence: 99%

“…The OFG decoder [20] sequentially processes the input, i.e., the pairs (f i , g i ), that are being provided by SNs, and executes Gaussian Elimination on the fly to progressively fill up G starting from an empty matrix, until G turns full rank, that corresponds to the recovery of the original sector.…”

Section: Pollution Detectionmentioning

confidence: 99%

Securing Coding-Based Cloud Storage Against Pollution Attacks

Anglano

Gaeta

Grangetto

2017

IEEE Trans. Parallel Distrib. Syst.

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“…But for small number of source symbols K , GE decoding could be used. There are two efficient versions of GE decoding in practice: Incremental Gaussian Elimination decoding [5] and On the Fly Gaussian Elimination (OFG) decoding [6]. GE decoding consists of two steps: triangular step and back-substitution step.…”

Section: Lt Codesmentioning

confidence: 99%

Improvement on OFG Decoding Method for LT Codes

Guo¹,

Wu²,

Wang³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Abstract-The digital fountain is a network encoding technology of an essentially infinite of encoded symbols, and LT codes is a class of codes that are useful in implementing an ideal digital fountain. Based on analysis of existing LT decoding algorithm, an improved method is proposed. At each encoded symbol arrivals, the new method performs both the triangular and backsubstitution steps of Gaussian elimination on the fly, thus distributing the decoding work during all symbols reception and obtaining a short delay.

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“…It will be successful if the set of equations is full rank. Compared with BP process in [18], ML decoding has lower decoding overhead but higher decoding complexity.…”

Section: Rateless Codesmentioning

confidence: 99%

“…One or more symbols can be contained in a packet. d Note that since we implement ML decoding of rateless codes, the overhead can be very low as k grows [18]. For example, when k > 500, < 0.01.…”

Section: Endnotesmentioning

confidence: 99%

Layered multimedia broadcast using rateless codes with progressive recovery over cooperative MIMO

Chen

Yin

et al. 2012

J Wireless Com Network

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This article proposes a novel scheme, based on unequal error protected rateless codes, for broadcasting layered multimedia over cooperative multiple-input multiple-output (MIMO). By taking advantage of both cooperation and broadcasting, we first present a two-phase cooperative MIMO broadcast scheme, which exploits distributed diversity in point-to-multipoint communication scenarios. Then, to enhance layered multimedia transmission with progressive recovery, the progressive rateless codes (PRC) is proposed to recover the layered data according to their importance at the expected received ratio of all output packets. Thus, receivers with different packet loss rate can achieve an adaptive recovery of the layered multimedia progressively. Furthermore, based on PRC, a distortion-based layered multimedia broadcast problem is formulated, which optimizes source bitrate of the layered multimedia and coding rates of PRC to improve the quality of experience (QoE) of the multimedia delivery over all receivers. The performance analysis including both analytical results and simulation experiments of Motion-JPEG 2000 broadcast to receivers verifies the superiority of the cooperative broadcast and efficiency of the QoE-driven optimization algorithm.

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On the fly gaussian elimination for LT codes

Cited by 76 publications

References 4 publications

Securing Coding-Based Cloud Storage Against Pollution Attacks

Securing Coding-Based Cloud Storage Against Pollution Attacks

Improvement on OFG Decoding Method for LT Codes

Layered multimedia broadcast using rateless codes with progressive recovery over cooperative MIMO

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