Deterministic Browser
August 22, 2017 ยท Declared Dead ยท ๐ Conference on Computer and Communications Security
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yinzhi Cao, Zhanhao Chen, Song Li, Shujiang Wu
arXiv ID
1708.06774
Category
cs.CR: Cryptography & Security
Citations
32
Venue
Conference on Computer and Communications Security
Last Checked
3 months ago
Abstract
Timing attacks have been a continuous threat to users' privacy in modern browsers. To mitigate such attacks, existing approaches, such as Tor Browser and Fermata, add jitters to the browser clock so that an attacker cannot accurately measure an event. However, such defenses only raise the bar for an attacker but do not fundamentally mitigate timing attacks, i.e., it just takes longer than previous to launch a timing attack. In this paper, we propose a novel approach, called deterministic browser, which can provably prevent timing attacks in modern browsers. Borrowing from Physics, we introduce several concepts, such as an observer and a reference frame. Specifically, a snippet of JavaScript, i.e., an observer in JavaScript reference frame, will always obtain the same, fixed timing information so that timing attacks are prevented; at contrast, a user, i.e., an oracle observer, will perceive the JavaScript differently and do not experience the performance slowdown. We have implemented a prototype called DeterFox and our evaluation shows that the prototype can defend against browser-related timing attacks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted