Return to the Zombie Gadgets: Undermining Destructive Code Reads via Code Inference Attacks

2016 ◽  
Author(s):  
Kevin Z. Snow ◽  
Roman Rogowski ◽  
Jan Werner ◽  
Hyungjoon Koo ◽  
Fabian Monrose ◽  
...  
Keyword(s):  
Inference Attacks

TEA-RNN: Topic-Enhanced Attentive RNN for Attribute Inference Attacks via User Behaviors

2021 ◽  
Author(s):  
Yifei Zhang ◽  
Jiong Wang ◽  
Junsha Chen ◽  
Neng Gao ◽  
Min Li ◽  
...  
Keyword(s):  
User Behaviors ◽  
Inference Attacks

scholarly journals Cross-path inference attacks on multipath TCP

2013 ◽  
Author(s):  
M. Zubair Shafiq ◽  
Franck Le ◽  
Mudhakar Srivatsa ◽  
Alex X. Liu
Keyword(s):  
Multipath Tcp ◽  
Inference Attacks

You Are What You Search: Attribute Inference Attacks Through Web Search Queries

2019 ◽  
pp. 343-358
Author(s):  
Tianyu Du ◽  
Tao Tao ◽  
Bijing Liu ◽  
Xueqi Jin ◽  
Jinfeng Li ◽  
...  
Keyword(s):  
Web Search ◽  
Search Queries ◽  
Inference Attacks

scholarly journals Algorithms that remember: model inversion attacks and data protection law

2018 ◽  
Vol 376 (2133) ◽  
pp. 20180083 ◽  
Author(s):  
Michael Veale ◽  
Reuben Binns ◽  
Lilian Edwards
Keyword(s):  
Data Protection ◽  
Personal Data ◽  
Training Data ◽  
Theme Issue ◽  
Future Directions ◽  
Model Inversion ◽  
General Data Protection Regulation ◽  
General Data ◽  
Inference Attacks ◽  
Use Of Models

Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around ‘model inversion’ and ‘membership inference’ attacks, which indicates that the process of turning training data into machine-learned systems is not one way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

scholarly journals Knowledge Discovery in Tweets for the Prevention of Inference Attacks

2017 ◽  
Vol 10 (34) ◽  
pp. 1-5
Author(s):  
D. Sai Eswari ◽  
Afreen Rafiq ◽  
R. Deepthi ◽  
◽  
◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Inference Attacks

scholarly journals Unleashing the Tiger: Inference Attacks on Split Learning

2021 ◽  
Author(s):  
Dario Pasquini ◽  
Giuseppe Ateniese ◽  
Massimo Bernaschi
Keyword(s):  
Inference Attacks

scholarly journals On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models

2021 ◽  
Author(s):  
Benjamin Zi Hao Zhao ◽  
Aviral Agrawal ◽  
Catisha Coburn ◽  
Hassan Jameel Asghar ◽  
Raghav Bhaskar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Inference Attacks ◽  
Machine Learning Models

SIA: Smartwatch-Enabled Inference Attacks on Physical Keyboards Using Acoustic Signals

2021 ◽  
Author(s):  
Ülkü Meteriz-Yıldıran ◽  
Necip Fazil Yildiran ◽  
David Mohaisen
Keyword(s):  
Acoustic Signals ◽  
Inference Attacks

Membership Inference Attacks Against Synthetic Health Data

2021 ◽  
pp. 103977
Author(s):  
Ziqi Zhang ◽  
Chao Yan ◽  
Bradley A. Malin
Keyword(s):  
Health Data ◽  
Inference Attacks
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