True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Laure Charleux; Katherine Schofield

doi:10.1080/15230406.2020.1794975

True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Laure Charleux, Katherine Schofield

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Spatial anonymization of address points is critical to fields such as public health. There have been recent concerns about applications of geomasks that did not guarantee the level of k-anonymity theoretically expected. An analysis of the problem and a potential solution were previously proposed: Adaptive Areal Elimination (AAE). The present paper expands on AAE and proposes a modified version, Adaptive Areal Masking (AAM). A benchmark comparison of both methods is conducted, which shows that AAM outperforms AAE in most configurations tested. The discussion attempts to identify the application cases for which AAE might still be preferable and addresses documentation needs with both methods.

Original language	English (US)
Pages (from-to)	537-549
Number of pages	13
Journal	Cartography and Geographic Information Science
Volume	47
Issue number	6
DOIs	https://doi.org/10.1080/15230406.2020.1794975
State	Published - Nov 1 2020

Bibliographical note

Funding Information:
The work for this paper was supported by a Pilot Project Grant through the Midwest Center for Occupational Health and Safety (MCOHS) Education and Research Center, University of Minnesota (UMN), Subaward NIOSH T42OH008434.

Keywords

Spatial anonymization
geomasking
k-anonymity
privacy
spatial aggregation

Access

10.1080/15230406.2020.1794975

OpenUrl availability

Full text

Cite this

@article{8a63c6aa3d674fafa142b2edc76c8552,

title = "True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking",

abstract = "Spatial anonymization of address points is critical to fields such as public health. There have been recent concerns about applications of geomasks that did not guarantee the level of k-anonymity theoretically expected. An analysis of the problem and a potential solution were previously proposed: Adaptive Areal Elimination (AAE). The present paper expands on AAE and proposes a modified version, Adaptive Areal Masking (AAM). A benchmark comparison of both methods is conducted, which shows that AAM outperforms AAE in most configurations tested. The discussion attempts to identify the application cases for which AAE might still be preferable and addresses documentation needs with both methods.",

keywords = "Spatial anonymization, geomasking, k-anonymity, privacy, spatial aggregation",

author = "Laure Charleux and Katherine Schofield",

note = "Funding Information: The work for this paper was supported by a Pilot Project Grant through the Midwest Center for Occupational Health and Safety (MCOHS) Education and Research Center, University of Minnesota (UMN), Subaward NIOSH T42OH008434.",

year = "2020",

month = nov,

day = "1",

doi = "10.1080/15230406.2020.1794975",

language = "English (US)",

volume = "47",

pages = "537--549",

journal = "Cartography and Geographic Information Science",

issn = "1523-0406",

publisher = "American Congress on Surveying and Mapping",

number = "6",

}

TY - JOUR

T1 - True spatial k-anonymity

T2 - adaptive areal elimination vs. adaptive areal masking

AU - Charleux, Laure

AU - Schofield, Katherine

N1 - Funding Information: The work for this paper was supported by a Pilot Project Grant through the Midwest Center for Occupational Health and Safety (MCOHS) Education and Research Center, University of Minnesota (UMN), Subaward NIOSH T42OH008434.

PY - 2020/11/1

Y1 - 2020/11/1

N2 - Spatial anonymization of address points is critical to fields such as public health. There have been recent concerns about applications of geomasks that did not guarantee the level of k-anonymity theoretically expected. An analysis of the problem and a potential solution were previously proposed: Adaptive Areal Elimination (AAE). The present paper expands on AAE and proposes a modified version, Adaptive Areal Masking (AAM). A benchmark comparison of both methods is conducted, which shows that AAM outperforms AAE in most configurations tested. The discussion attempts to identify the application cases for which AAE might still be preferable and addresses documentation needs with both methods.

AB - Spatial anonymization of address points is critical to fields such as public health. There have been recent concerns about applications of geomasks that did not guarantee the level of k-anonymity theoretically expected. An analysis of the problem and a potential solution were previously proposed: Adaptive Areal Elimination (AAE). The present paper expands on AAE and proposes a modified version, Adaptive Areal Masking (AAM). A benchmark comparison of both methods is conducted, which shows that AAM outperforms AAE in most configurations tested. The discussion attempts to identify the application cases for which AAE might still be preferable and addresses documentation needs with both methods.

KW - Spatial anonymization

KW - geomasking

KW - k-anonymity

KW - privacy

KW - spatial aggregation

UR - http://www.scopus.com/inward/record.url?scp=85089446376&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85089446376&partnerID=8YFLogxK

U2 - 10.1080/15230406.2020.1794975

DO - 10.1080/15230406.2020.1794975

M3 - Article

AN - SCOPUS:85089446376

SN - 1523-0406

VL - 47

SP - 537

EP - 549

JO - Cartography and Geographic Information Science

JF - Cartography and Geographic Information Science

IS - 6

ER -

True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Abstract

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this