Abstract
Maximal α-leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the accuracy of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. To study the effect of side information on this measure, we introduce and define conditional maximal α-leakage. We show that, for a chosen mapping (channel) from the actual (viewed as private) data to the released (public) data and some side information, the conditional maximal α-leakage is the supremum (over all side information) of the conditional Arimoto channel capacity where the conditioning is on the side information. We prove that if the side information is conditionally independent of the public data given the private data, the side information cannot increase the information leakage.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 642-646 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538692912 |
| DOIs | |
| State | Published - Jul 2019 |
| Event | 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France Duration: Jul 7 2019 → Jul 12 2019 |
Publication series
| Name | IEEE International Symposium on Information Theory - Proceedings |
|---|---|
| Volume | 2019-July |
| ISSN (Print) | 2157-8095 |
Conference
| Conference | 2019 IEEE International Symposium on Information Theory, ISIT 2019 |
|---|---|
| Country | France |
| City | Paris |
| Period | 7/7/19 → 7/12/19 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics
Cite this
Robustness of Maximal α-Leakage to Side Information. / Liao, Jiachun; Sankar, Lalitha; Kosut, Oliver; Calmon, Flavio P.
2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 642-646 8849769 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2019-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Robustness of Maximal α-Leakage to Side Information
AU - Liao, Jiachun
AU - Sankar, Lalitha
AU - Kosut, Oliver
AU - Calmon, Flavio P.
PY - 2019/7
Y1 - 2019/7
N2 - Maximal α-leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the accuracy of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. To study the effect of side information on this measure, we introduce and define conditional maximal α-leakage. We show that, for a chosen mapping (channel) from the actual (viewed as private) data to the released (public) data and some side information, the conditional maximal α-leakage is the supremum (over all side information) of the conditional Arimoto channel capacity where the conditioning is on the side information. We prove that if the side information is conditionally independent of the public data given the private data, the side information cannot increase the information leakage.
AB - Maximal α-leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the accuracy of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. To study the effect of side information on this measure, we introduce and define conditional maximal α-leakage. We show that, for a chosen mapping (channel) from the actual (viewed as private) data to the released (public) data and some side information, the conditional maximal α-leakage is the supremum (over all side information) of the conditional Arimoto channel capacity where the conditioning is on the side information. We prove that if the side information is conditionally independent of the public data given the private data, the side information cannot increase the information leakage.
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UR - http://www.scopus.com/inward/citedby.url?scp=85073149343&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2019.8849769
DO - 10.1109/ISIT.2019.8849769
M3 - Conference contribution
AN - SCOPUS:85073149343
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 642
EP - 646
BT - 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -