文献データベース

Multi-Dimensional Randomized Response

Authors: Josep Domingo-Ferrer, Jordi Soria-Comas | Published: 2020-10-21 | Updated: 2020-12-19
データセット評価
プライバシー保証
多次元データ分析

VenoMave: Targeted Poisoning Against Speech Recognition

Authors: Hojjat Aghakhani, Lea Schönherr, Thorsten Eisenhofer, Dorothea Kolossa, Thorsten Holz, Christopher Kruegel, Giovanni Vigna | Published: 2020-10-21 | Updated: 2023-04-20
バックドア攻撃
ポイズニング
ポイズニング攻撃

Towards Understanding the Dynamics of the First-Order Adversaries

Authors: Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su | Published: 2020-10-20
対抗的学習
最適化手法
重み更新手法

Mitigating Sybil Attacks on Differential Privacy based Federated Learning

Authors: Yupeng Jiang, Yong Li, Yipeng Zhou, Xi Zheng | Published: 2020-10-20
DFLに対する攻撃手法
ポイズニング
ポイズニング攻撃

Tight Second-Order Certificates for Randomized Smoothing

Authors: Alexander Levine, Aounon Kumar, Thomas Goldstein, Soheil Feizi | Published: 2020-10-20 | Updated: 2020-12-15
最適化手法
特徴重要度分析
証明書の比率分析

Image Obfuscation for Privacy-Preserving Machine Learning

Authors: Mathilde Raynal, Radhakrishna Achanta, Mathias Humbert | Published: 2020-10-20
データセット評価
データ保護手法
メンバーシップ推論

A Survey of Machine Learning Techniques in Adversarial Image Forensics

Authors: Ehsan Nowroozi, Ali Dehghantanha, Reza M. Parizi, Kim-Kwang Raymond Choo | Published: 2020-10-19
ポイズニング
対抗的学習
敵対的サンプル

Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification

Authors: Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck | Published: 2020-10-19
PEマルウェア分類
バックドア攻撃
マルウェア分類

Dos and Don’ts of Machine Learning in Computer Security

Authors: Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck | Published: 2020-10-19 | Updated: 2021-11-30
スプリアス相関
データセット評価
バイアス

Privacy-preserving Data Sharing on Vertically Partitioned Data

Authors: Razane Tajeddine, Joonas Jälkö, Samuel Kaski, Antti Honkela | Published: 2020-10-19 | Updated: 2022-09-02
数値安定性の問題
最適化手法
機械学習のプライバシー保護