Plausible Deniability

David Sidi; Jane Bambauer

doi:10.1007/978-3-030-57521-2_7

Plausible Deniability

David Sidi, Jane Bambauer

Law Instruction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.

Original language	English (US)
Title of host publication	Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings
Editors	Josep Domingo-Ferrer, Krishnamurty Muralidhar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	91-105
Number of pages	15
ISBN (Print)	9783030575205
DOIs	https://doi.org/10.1007/978-3-030-57521-2_7
State	Published - 2020
Event	International Conference on Privacy in Statistical Databases, PSD 2020 - Tarragona, Spain Duration: Sep 23 2020 → Sep 25 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12276 LNCS

Conference

Conference	International Conference on Privacy in Statistical Databases, PSD 2020
Country/Territory	Spain
City	Tarragona
Period	9/23/20 → 9/25/20

Keywords

Education data
Simulation
Statistical disclosure control

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-57521-2_7

Cite this

Sidi, D., & Bambauer, J. (2020). Plausible Deniability. In J. Domingo-Ferrer, & K. Muralidhar (Eds.), Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings (pp. 91-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12276 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57521-2_7

Plausible Deniability. / Sidi, David; Bambauer, Jane.
Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. ed. / Josep Domingo-Ferrer; Krishnamurty Muralidhar. Springer Science and Business Media Deutschland GmbH, 2020. p. 91-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12276 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sidi, D & Bambauer, J 2020, Plausible Deniability. in J Domingo-Ferrer & K Muralidhar (eds), Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12276 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 91-105, International Conference on Privacy in Statistical Databases, PSD 2020, Tarragona, Spain, 9/23/20. https://doi.org/10.1007/978-3-030-57521-2_7

Sidi D, Bambauer J. Plausible Deniability. In Domingo-Ferrer J, Muralidhar K, editors, Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 91-105. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-57521-2_7

Sidi, David ; Bambauer, Jane. / Plausible Deniability. Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings. editor / Josep Domingo-Ferrer ; Krishnamurty Muralidhar. Springer Science and Business Media Deutschland GmbH, 2020. pp. 91-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c41fadb8ea1e42a7a5d13a1727dd3fa8,

title = "Plausible Deniability",

abstract = "From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.",

keywords = "Education data, Simulation, Statistical disclosure control",

author = "David Sidi and Jane Bambauer",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Privacy in Statistical Databases, PSD 2020 ; Conference date: 23-09-2020 Through 25-09-2020",

year = "2020",

doi = "10.1007/978-3-030-57521-2_7",

language = "English (US)",

isbn = "9783030575205",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "91--105",

editor = "Josep Domingo-Ferrer and Krishnamurty Muralidhar",

booktitle = "Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings",

address = "Germany",

}

TY - GEN

T1 - Plausible Deniability

AU - Sidi, David

AU - Bambauer, Jane

PY - 2020

Y1 - 2020

N2 - From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.

AB - From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.

KW - Education data

KW - Simulation

KW - Statistical disclosure control

UR - http://www.scopus.com/inward/record.url?scp=85092093268&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85092093268&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-57521-2_7

DO - 10.1007/978-3-030-57521-2_7

M3 - Conference contribution

SN - 9783030575205

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 91

EP - 105

BT - Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings

A2 - Domingo-Ferrer, Josep

A2 - Muralidhar, Krishnamurty

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Privacy in Statistical Databases, PSD 2020

Y2 - 23 September 2020 through 25 September 2020

ER -

Plausible Deniability

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this