Dec 3, 2022 � Our experiments demonstrate that LDL reduces the success rate of an adversary carrying out a LAB MIA in each case. We empirically compare LDL�...
The data used to train deep neural network (DNN) models in applications such as healthcare and finance typically contain sensitive information.
The objective of LDL is to ensure that magnitudes of noise required for misclassification of members and nonmembers are comparable. This will ensure�...
This sphere of label-invariance creates ambiguity and prevents a querying adversary from correctly determining whether a sample is a member or a nonmember. We�...
LDL: A Defense for Label-Based Membership Inference Attacks ... The data used to train deep neural network (DNN) models in applications such as healthcare and�...
Dec 3, 2022 � MIAs aim to determine whether a sample belongs to the dataset used to train a classifier (members) or not (nonmembers). Recently, a new class of�...
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What is a membership inference attack?
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(3) As the boundary attack requires accurate distance estimation, Rajabi et al. (2022) proposed a defense called LDL against it. LDL creates a hyper-sphere�...
Poovendran, "LDL: A Defense for Label-Based Membership Inference Attacks," ACM Asia Conference on Computer and Communications Security (AsiaCCS), July 2023.
Mar 13, 2022 � This paper proposes a differentially private defense method that handles both types of attacks in a time-efficient manner by tuning only one�...
Our experiments show that training with differential privacy or strong L2 regularization are the only current defenses that meaningfully decrease leakage of�...
Missing: LDL: Based