Secure Distributed Computing With Straggling Servers Using Polynomial Codes

Yang, Heecheol; Lee, Jung Woo

doi:10.1109/tifs.2018.2846601

Cited by 116 publications

(62 citation statements)

References 21 publications

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“…The main result recovers prior art, including [19]- [21]. Among topics for future work, we mention here the establishment of matching converse bounds [16] and the consideration of impairments in the communication channel between workers [25].…”

Section: A Secure Generalized Polydot Code: the S < T Casesupporting

confidence: 70%

Distributed and Private Coded Matrix Computation with Flexible Communication Load

Aliasgari

Simeone

Kliewer

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. For user-driven tasks these operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold and communication load. In this paper we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

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Section: A Secure Generalized Polydot Code: the S < T Casesupporting

confidence: 70%

Distributed and Private Coded Matrix Computation with Flexible Communication Load

Aliasgari

Simeone

Kliewer

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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“…Codes for privacy and straggler mitigation in distributed computing are first introduced in [3,26] where the authors consider a homogeneous setting and focus on matrixvector multiplication. The problem of private distributed matrix-matrix multiplication and private polynomial computation with straggler tolerance is studied [23,[55][56][57][58][59]. In the private matrix-matrix multiplication setting, the master wants to simultaneously maintain the privacy of both matrices which is a generalization of the matrix-vector multiplication setting.…”

Section: Related Workmentioning

confidence: 99%

Private and rateless adaptive coded matrix-vector multiplication

Bitar

Xing

Keshtkarjahromi

et al. 2021

J Wireless Com Network

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Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.

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“…For P C ≥ 1 this implies the recovery threshold P R in (22). The communication load C L in (16) follows from the fact that there…”

Section: A Secure Generalized Polydot Code: the S < T Casementioning

confidence: 99%

Private and Secure Distributed Matrix Multiplication With Flexible Communication Load

Aliasgari

Simeone

Kliewer

2020

IEEE Trans.Inform.Forensic Secur.

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Large matrix multiplications are central to largescale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, i.e., the total amount of data to be downloaded from the workers. In this paper, in addition to exact recovery requirements, we impose security and privacy constraints on the data matrices, and study the recovery threshold as a function of the communication load. We first assume that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, we introduce a novel class of secure codes, referred to as secure generalized PolyDot (SGPD) codes, that generalize state-of-the-art non-secure codes for matrix multiplication. SGPD codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. We then assume that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, we present a variant of generalized PolyDot codes that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers.

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Secure Distributed Computing With Straggling Servers Using Polynomial Codes

Cited by 116 publications

References 21 publications

Distributed and Private Coded Matrix Computation with Flexible Communication Load

Distributed and Private Coded Matrix Computation with Flexible Communication Load

Private and rateless adaptive coded matrix-vector multiplication

Private and Secure Distributed Matrix Multiplication With Flexible Communication Load

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