TY - GEN
T1 - Maximal α-Leakage and its Properties
AU - Liao, Jiachun
AU - Sankar, Lalitha
AU - Kosut, Oliver
AU - Calmon, Flavio P.
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. CCF-1901243.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Maximal α-leakage is a tunable measure of information leakage based on the quality of an adversary's belief about an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the quality of a belief, ranging from log-loss at α=1 to the probability of error at α=∞. We review its definition and main properties, including extensions to α< 1, robustness to side information, and relationship to Rènyi differential privacy.
AB - Maximal α-leakage is a tunable measure of information leakage based on the quality of an adversary's belief about an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the quality of a belief, ranging from log-loss at α=1 to the probability of error at α=∞. We review its definition and main properties, including extensions to α< 1, robustness to side information, and relationship to Rènyi differential privacy.
UR - http://www.scopus.com/inward/record.url?scp=85090134722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090134722&partnerID=8YFLogxK
U2 - 10.1109/CNS48642.2020.9162168
DO - 10.1109/CNS48642.2020.9162168
M3 - Conference contribution
AN - SCOPUS:85090134722
T3 - 2020 IEEE Conference on Communications and Network Security, CNS 2020
BT - 2020 IEEE Conference on Communications and Network Security, CNS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Conference on Communications and Network Security, CNS 2020
Y2 - 29 June 2020 through 1 July 2020
ER -