Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting

• URL: http://arxiv.org/abs/2411.10059v1
• Date: Fri, 15 Nov 2024 09:22:14 GMT
• Title: Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting
• Authors: Andreas Athanasiou, Konstantinos Chatzikokolakis, Catuscia Palamidessi,
• Abstract summary: Quantitative Information Flow (QIF) provides a robust information-theoretical framework for designing secure systems with minimal information leakage. We propose optimal solutions for constructing a new row, in a known and unmodifiable information-theoretic channel, aiming at minimizing the leakage. We apply our approach to the problem of website fingerprinting defense, considering a scenario where a site administrator can modify their own site but not others.
• Score: 8.227044921274494
• License:
• Abstract: Quantitative Information Flow (QIF) provides a robust information-theoretical framework for designing secure systems with minimal information leakage. While previous research has addressed the design of such systems under hard constraints (e.g. application limitations) and soft constraints (e.g. utility), scenarios often arise where the core system's behavior is considered fixed. In such cases, the challenge is to design a new component for the existing system that minimizes leakage without altering the original system. In this work we address this problem by proposing optimal solutions for constructing a new row, in a known and unmodifiable information-theoretic channel, aiming at minimizing the leakage. We first model two types of adversaries: an exact-guessing adversary, aiming to guess the secret in one try, and a s-distinguishing one, which tries to distinguish the secret s from all the other secrets.Then, we discuss design strategies for both fixed and unknown priors by offering, for each adversary, an optimal solution under linear constraints, using Linear Programming.We apply our approach to the problem of website fingerprinting defense, considering a scenario where a site administrator can modify their own site but not others. We experimentally evaluate our proposed solutions against other natural approaches. First, we sample real-world news websites and then, for both adversaries, we demonstrate that the proposed solutions are effective in achieving the least leakage. Finally, we simulate an actual attack by training an ML classifier for the s-distinguishing adversary and show that our approach decreases the accuracy of the attacker.

Related papers

• Automated Phishing Detection Using URLs and Webpages [35.66275851732625]
  This project addresses the constraints of traditional reference-based phishing detection by developing an LLM agent framework. This agent harnesses Large Language Models to actively fetch and utilize online information. Our approach has achieved with accuracy of 0.945, significantly outperforms the existing solution(DynaPhish) by 0.445.
  arXiv  Detail & Related papers  (2024-08-03T05:08:27Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data. The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• A Proactive Decoy Selection Scheme for Cyber Deception using MITRE ATT&CK [0.9831489366502301]
  Cyber deception allows compensating the late response of defenders to the ever evolving tactics, techniques, and procedures (TTPs) of attackers. In this work, we design a decoy selection scheme that is supported by an adversarial modeling based on empirical observation of real-world attackers. Results reveal that the proposed scheme provides the highest interception rate of attack paths using the lowest amount of decoys.
  arXiv  Detail & Related papers  (2024-04-19T10:45:05Z)
• Discriminative Adversarial Unlearning [40.30974185546541]
  We introduce a novel machine unlearning framework founded upon the established principles of the min-max optimization paradigm. We capitalize on the capabilities of strong Membership Inference Attacks (MIA) to facilitate the unlearning of specific samples from a trained model. Our proposed algorithm closely approximates the ideal benchmark of retraining from scratch for both random sample forgetting and class-wise forgetting schemes.
  arXiv  Detail & Related papers  (2024-02-10T03:04:57Z)
• A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space [13.096022606256973]
  We propose a unified framework to generate feasible adversarial examples that satisfy given domain constraints. Our framework forms the starting point for research on constrained adversarial attacks and provides relevant baselines and datasets that research can exploit.
  arXiv  Detail & Related papers  (2021-12-02T12:05:27Z)
• Online Adversarial Attacks [57.448101834579624]
  We formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases. We first rigorously analyze a deterministic variant of the online threat model. We then propose algoname, a simple yet practical algorithm yielding a provably better competitive ratio for $k=2$ over the current best single threshold algorithm.
  arXiv  Detail & Related papers  (2021-03-02T20:36:04Z)
• Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits [55.740716446995805]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Our goal is to misclassify a specific sample into a target class without any sample modification. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
  arXiv  Detail & Related papers  (2021-02-21T03:13:27Z)
• A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning [122.49765136434353]
  We present an effective method, called Hamiltonian Monte Carlo with Accumulated Momentum (HMCAM), aiming to generate a sequence of adversarial examples. We also propose a new generative method called Contrastive Adversarial Training (CAT), which approaches equilibrium distribution of adversarial examples. Both quantitative and qualitative analysis on several natural image datasets and practical systems have confirmed the superiority of the proposed algorithm.
  arXiv  Detail & Related papers  (2020-10-15T16:07:26Z)
• Topology-aware Differential Privacy for Decentralized Image Classification [81.2202290003513]
  Top-DP is a novel solution to optimize the differential privacy protection of decentralized image classification systems. We leverage the unique features of decentralized communication topologies to reduce the noise scale and improve the model usability.
  arXiv  Detail & Related papers  (2020-06-14T06:42:21Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.