Security and Communication Networks

Security and Communication Networks / 2021 / Article

Corrigendum | Open Access

Volume 2021 |Article ID 9874067 | https://doi.org/10.1155/2021/9874067

Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu, "Corrigendum to “A Comprehensive Survey on Local Differential Privacy”", Security and Communication Networks, vol. 2021, Article ID 9874067, 2 pages, 2021. https://doi.org/10.1155/2021/9874067

Corrigendum to “A Comprehensive Survey on Local Differential Privacy”

Received10 May 2021
Accepted10 May 2021
Published29 May 2021

In the article titled “A Comprehensive Survey on Local Differential Privacy” [1], the authors identified a number of errors in the communication cost and computation cost as well as pros and cons columns of Table 4. These errors were introduced during the preparation of the manuscript and do not impact the conclusions. The corrected Table 4 is as follows:


MethodEncodeRandomnessAsymptotic bound errorCandidateCommunication costComputation costPros and cons

k-RR [35]
GRR [36]
DirectLocalKnownP: O(1)
S: O(n)
P: O(1)
S: O(n + k)
Pros: no encoding, predigest the process; lower candidate size can achieve higher utility; cons: low utility in low privacy regime
O-RR [35]Unary (bloom filter)LocalUnknownP: O(h)
S: O(nh)
P: O(k)
S: linear regression
Pros: open candidate; cons: low utility in low privacy regime, high computation cost due to regression
RAPPOR [7]Unary (bloom filter)LocalKnownP: O(h)
S: O(nh)
P: O(k)
S: LASSO and linear regression
Pros: lower error, lower storage cost, support big candidate; cons: consider bloom filter parameter settings, high computation cost due to regression
k-RAPPOR (basic one-time) [7]UnaryLocalKnownP: Θ(k)
S: O(nk)
P: O(k)
S:
Pros: lower error, lower storage overhead, simpler and faster implement; cons: consider parameter settings of bloom filter
OUE [36]UnaryLocalKnownP: Θ(k)
S: O(nk)
P: O(k)
S:
Pros: lower error, lower storage cost, lower computation cost and easier to implement; cons: larger candidate lead to higher communication cost
O-RAPPOR [35]Unary (bloom filter)LocalUnknownP: Θ(h)
S: O(nh)
P: O(k)
S: linear regression
Pros: open candidate, higher utility, lower storage overhead; cons: need consider parameter settings of bloom filter
k-Subset [41, 42]DirectLocalKnownP: Θ(k)
S: O(nk)
P: O(k)
S:
Pros: better sample complexity and higher utility; cons: higher communication and computation cost due to set output
RMP(SHist) [37]BinaryPublic (shared matrix)KnownP: O(1)
S: O(n)
P: O(k)
S: O(nk)
Pros: lower communication cost; cons: Unstable query accuracy due to the noise from RMP matric
HRR [10, 38]BinaryPublic (shared matrix)KnownP: O(1)
S: O(n)
P: O(k)
S: O(nk)
Pros: lower communication cost; cons: unable query accuracy due to the noise from RMP matric
BLH [36]BinaryLocal and publicKnownP: O(1)
S: Θ(log(n))
P: O(k)
S: O(nk)
Pros: lower communication cost; cons: higher computation overhead due to the Hashing
OLH [36]BinaryLocal and publicUnknownP: O(1)
S: Θ(log(n))
P: O(k)
S: O(nk)
Pros: higher utility in the setting big candidate size, lower communication cost; cons: higher computation overhead due to the Hashing
HR [39]BinaryLocalKnownP: O(log(k))
S: (O(nlog(k))
P: O(k)
S: O(n+k)
Pros: obtain efficient computation complexity due to fast walsh-hadamard transform; cons: unstable accuracy due to the noise from encoding

References

  1. X. Xiong, S. Liu, D. Li, Z. Cai, and X. Niu, “A Comprehensive Survey on Local Differential Privacy,” Security and Communication Networks, vol. 2020, Article ID 8829523, 29 pages, 2020. View at: Publisher Site | Google Scholar

Copyright © 2021 Xingxing Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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