Differential Privacy: What is all the noise about?

• URL: http://arxiv.org/abs/2205.09453v1
• Date: Thu, 19 May 2022 10:12:29 GMT
• Title: Differential Privacy: What is all the noise about?
• Authors: Roxana Danger
• Abstract summary: Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. This paper aims to provide an overview of the most important ideas, concepts and uses of DP in Machine Learning (ML)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. It makes no assumptions about the knowledge or computational power of adversaries, and provides an interpretable, quantifiable and composable formalism. DP has been actively researched during the last 15 years, but it is still hard to master for many Machine Learning (ML)) practitioners. This paper aims to provide an overview of the most important ideas, concepts and uses of DP in ML, with special focus on its intersection with Federated Learning (FL).

Related papers

• The Limits of Differential Privacy in Online Learning [11.099792269219124]
  We present evidence that separates three types of constraints: no DP, pure DP, and approximate DP. We first describe a hypothesis class that is online learnable under approximate DP but not online learnable under pure DP under the adaptive adversarial setting. We then prove that any private online learner must make an infinite number of mistakes for almost all hypothesis classes.
  arXiv  Detail & Related papers  (2024-11-08T11:21:31Z)
• Differential Privacy Overview and Fundamental Techniques [63.0409690498569]
  This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed. It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis.
  arXiv  Detail & Related papers  (2024-11-07T13:52:11Z)
• Masked Differential Privacy [64.32494202656801]
  We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied. Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• 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)
• Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory [82.7042006247124]
  We show that even the most capable AI models reveal private information in contexts that humans would not, 39% and 57% of the time, respectively. Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
  arXiv  Detail & Related papers  (2023-10-27T04:15:30Z)
• How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy [22.906644117887133]
  Differential Privacy (DP) has become a gold standard for making formal statements about data anonymization. The adoption of DP is hindered by limited practical guidance of what DP protection entails, what privacy guarantees to aim for, and the difficulty of achieving good privacy-utility-computation trade-offs for ML models. This work is a self-contained guide that gives an in-depth overview of the field of DP ML and presents information about achieving the best possible DP ML model with rigorous privacy guarantees.
  arXiv  Detail & Related papers  (2023-03-01T16:56:39Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning [5.769445676575767]
  We review the use of differential privacy (DP) for privacy protection in machine learning (ML) We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loose that they do not offer the ex ante privacy guarantees of DP.
  arXiv  Detail & Related papers  (2022-06-09T17:13:10Z)
• Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning [0.0]
  Differential privacy is a formal definition that allows quantifying the privacy-utility trade-off. With the local DP (LDP) model, users can sanitize their data locally before transmitting it to the server. In all cases, we concluded that differentially private ML models achieve nearly the same utility metrics as non-private ones.
  arXiv  Detail & Related papers  (2022-04-02T12:50:14Z)
• How reparametrization trick broke differentially-private text representation leaning [2.45626162429986]
  differential privacy is one of the favorite approaches to privacy-preserving methods in NLP. Despite its simplicity, it seems non-trivial to get it right when applying it to NLP. Our main goal is to raise awareness and help the community understand potential pitfalls of applying differential privacy to text representation learning.
  arXiv  Detail & Related papers  (2022-02-24T15:02:42Z)
• 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.