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On the amplification of security and privacy risks by post-hoc
  explanations in machine learning models

On the amplification of security and privacy risks by post-hoc explanations in machine learning models

28 June 2022
Pengrui Quan
Supriyo Chakraborty
J. Jeyakumar
Mani B. Srivastava
    MIACV
    AAML
ArXivPDFHTML

Papers citing "On the amplification of security and privacy risks by post-hoc explanations in machine learning models"

3 / 3 papers shown
Title
Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
Qizhang Feng
Siva Rajesh Kasa
Santhosh Kumar Kasa
Hyokun Yun
C. Teo
S. Bodapati
92
7
0
08 Jul 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu
S. Szyller
Nadarajah Asokan
AAML
52
2
0
07 Dec 2023
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Minhao Cheng
Simranjit Singh
Patrick H. Chen
Pin-Yu Chen
Sijia Liu
Cho-Jui Hsieh
AAML
134
219
0
24 Sep 2019
1