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Applying Differential Privacy to Machine Learning: Challenges and Potentials.

. Krypto-Tag, Gesellschaft für Informatik e.V. / FG KRYPTO, (2019)

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Olaf Boenisch

Ulrich Boenisch

Jens Boenisch

Vasco Boenisch

Herbert Boenisch

 

Другие публикации лиц с тем же именем

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation., , , , , и . EuroS&P, стр. 241-257. IEEE, (2023)Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks., , , , , и . CoRR, (2024)Applying Differential Privacy to Machine Learning: Challenges and Potentials.. Krypto-Tag, Gesellschaft für Informatik e.V. / FG KRYPTO, (2019)Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees., , , , и . Proc. Priv. Enhancing Technol., 2023 (1): 158-176 (января 2023)Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders., , , , и . CoRR, (2023)Secure and Private Machine Learning.. Free University of Berlin, Germany, (2022)When the Curious Abandon Honesty: Federated Learning Is Not Private., , , , , и . EuroS&P, стр. 175-199. IEEE, (2023)Side-Channel Attacks on Query-Based Data Anonymization., , , , , и . CCS, стр. 1254-1265. ACM, (2021)The Influence of Training Parameters on Neural Networks' Vulnerability to Membership Inference Attacks., и . GI-Jahrestagung, том P-326 из LNI, стр. 1227-1246. Gesellschaft für Informatik, Bonn, (2022)Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning., , , и . CoRR, (2024)