A Framework for Differential Privacy Against Timing Attacks

• URL: http://arxiv.org/abs/2409.05623v1
• Date: Mon, 9 Sep 2024 13:56:04 GMT
• Title: A Framework for Differential Privacy Against Timing Attacks
• Authors: Zachary Ratliff, Salil Vadhan,
• Abstract summary: We establish a general framework for ensuring differential privacy in the presence of timing side channels. We define a new notion of timing privacy, which captures programs that remain differentially private to an adversary. We show how our framework can be realized in code through a natural extension of the OpenDP Programming Framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The standard definition of differential privacy (DP) ensures that a mechanism's output distribution on adjacent datasets is indistinguishable. However, real-world implementations of DP can, and often do, reveal information through their runtime distributions, making them susceptible to timing attacks. In this work, we establish a general framework for ensuring differential privacy in the presence of timing side channels. We define a new notion of timing privacy, which captures programs that remain differentially private to an adversary that observes the program's runtime in addition to the output. Our framework enables chaining together component programs that are timing-stable followed by a random delay to obtain DP programs that achieve timing privacy. Importantly, our definitions allow for measuring timing privacy and output privacy using different privacy measures. We illustrate how to instantiate our framework by giving programs for standard DP computations in the RAM and Word RAM models of computation. Furthermore, we show how our framework can be realized in code through a natural extension of the OpenDP Programming Framework.

Related papers

• Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy [55.357715095623554]
  Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. We propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification.
  arXiv  Detail & Related papers  (2024-10-24T03:39:55Z)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy [14.408776376918484]
  Local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. We propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use.
  arXiv  Detail & Related papers  (2024-04-05T03:22:47Z)
• Provable Privacy with Non-Private Pre-Processing [56.770023668379615]
  We propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions.
  arXiv  Detail & Related papers  (2024-03-19T17:54:49Z)
• Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification [54.1447806347273]
  Amplification by subsampling is one of the main primitives in machine learning with differential privacy. We propose the first general framework for deriving mechanism-specific guarantees. We analyze how subsampling affects the privacy of groups of multiple users.
  arXiv  Detail & Related papers  (2024-03-07T19:36:05Z)
• A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management [23.847568516724937]
  We introduce a new privacy-preserving technique that uses a deep learning model trained using Differentially-Private Descent (DP-SGD) algorithm. We then demonstrate a novel declarative privacy-preserving workflow that allows users to specify "what private information to protect" rather than "how to protect"
  arXiv  Detail & Related papers  (2024-01-22T22:50:59Z)
• Privacy Amplification via Shuffling for Linear Contextual Bandits [51.94904361874446]
  We study the contextual linear bandit problem with differential privacy (DP) We show that it is possible to achieve a privacy/utility trade-off between JDP and LDP by leveraging the shuffle model of privacy. Our result shows that it is possible to obtain a tradeoff between JDP and LDP by leveraging the shuffle model while preserving local privacy.
  arXiv  Detail & Related papers  (2021-12-11T15:23:28Z)
• Private Reinforcement Learning with PAC and Regret Guarantees [69.4202374491817]
  We design privacy preserving exploration policies for episodic reinforcement learning (RL) We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP) We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee.
  arXiv  Detail & Related papers  (2020-09-18T20:18:35Z)

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.