On Secure Distributed Linearly Separable Computation

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. The data center assigns...

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Published in:IEEE journal on selected areas in communications Vol. 40; no. 3; pp. 912 - 926
Main Authors: Wan, Kai, Sun, Hua, Ji, Mingyue, Caire, Giuseppe
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Communication
Computation
Costs
Data centers
Datasets
Distributed computing
Encoding
Information theory
Optimization
Randomness
Security
Servers
straggler mitigation
Sun
Task analysis
ISSN:0733-8716, 1558-0008
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Abstract Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. The data center assigns a subset of input datasets to each server in an uncoded manner, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment . These schemes are optimal subject to using these assignments. Motivated by the observation of the general limitation of these two schemes on the randomness size, we propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.
AbstractList Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. The data center assigns a subset of input datasets to each server in an uncoded manner, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment . These schemes are optimal subject to using these assignments. Motivated by the observation of the general limitation of these two schemes on the randomness size, we propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.
Author Wan, Kai
Caire, Giuseppe
Ji, Mingyue
Sun, Hua
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SubjectTerms Communication
Computation
Costs
Data centers
Datasets
Distributed computing
Encoding
Information theory
Optimization
Randomness
Security
Servers
straggler mitigation
Sun
Task analysis
Title On Secure Distributed Linearly Separable Computation
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