Security and Communication Networks
Volume 2021 (2021), Article ID 9874067, 2 pages
https://doi.org/10.1155/2021/9874067

Corrigendum

Corrigendum to “A Comprehensive Survey on Local Differential Privacy”

Xingxing Xiong,¹ Shubo Liu,¹ Dan Li,1,2 Zhaohui Cai,¹ and Xiaoguang Niu¹

¹School of Computer Science, Wuhan University, Wuhan 430072, China
²Hubei Water Resources Research Institution, Wuhan 430070, China

Correspondence should be addressed to Shubo Liu; liu.shubo@whu.edu.cn

Received 10 May 2021; Accepted 10 May 2021; Published 29 May 2021

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.

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:

Table 4: Comparisons of frequency oracle mechanisms for frequency estimation under LDP.

Method
Encode
Randomness
Asymptotic bound error
Candidate
Communication cost
Computation cost
Pros and cons

k-RR [35]
GRR [36]
Direct
Local

Known
P: 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)
Local

Unknown
P: 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)
Local

Known
P: 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]
Unary
Local

Known
P: 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]
Unary
Local

Known
P: 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)
Local

Unknown
P: 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]
Direct
Local

Known
P: 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]
Binary
Public (shared matrix)

Known
P: 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]
Binary
Public (shared matrix)

Known
P: 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]
Binary
Local and public

Known
P: 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]
Binary
Local and public

Unknown
P: 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]
Binary
Local

Known
P: 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

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.