Corrigendum to “A Comprehensive Survey on Local Differential Privacy”

Xiong, Xingxing; Liu, Shubo; Li, Dan; Cai, Zhaohui; Niu, Xiaoguang

doi:https://doi.org/10.1155/2021/9874067

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:


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. View at: Publisher Site | Google Scholar

Copyright

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.

Security and Communication Networks

Corrigendum to “A Comprehensive Survey on Local Differential Privacy”

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