Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey

• URL: http://arxiv.org/abs/2309.16398v1
• Date: Thu, 28 Sep 2023 12:44:59 GMT
• Title: Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
• Authors: Lea Demelius, Roman Kern, Andreas Tr\"ugler
• Abstract summary: Differential Privacy has become a widely popular method for data protection in machine learning. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning.
• Score: 1.89915151018241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: auditing and evaluation methods for private models, improvements of privacy-utility trade-offs, protection against a broad range of threats and attacks, differentially private generative models, and emerging application domains.

Related papers

• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook [54.24701201956833]
  Security and privacy issues have undermined users' confidence in pre-trained models. Current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models. This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches.
  arXiv  Detail & Related papers  (2024-11-12T10:15:33Z)
• 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)
• Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions [12.451936012379319]
  Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. Their reliance on massive internet-sourced datasets for training brings notable privacy issues. Certain application-specific scenarios may require fine-tuning these models on private data.
  arXiv  Detail & Related papers  (2024-08-10T05:41:19Z)
• A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks [42.3024294376025]
  Machine unlearning is a research hotspot in the field of privacy protection. Recent researchers have found potential privacy leakages of various of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.
  arXiv  Detail & Related papers  (2024-06-10T11:31:04Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• A General Framework for Auditing Differentially Private Machine Learning [27.99806936918949]
  We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations.
  arXiv  Detail & Related papers  (2022-10-16T21:34:18Z)
• How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing [0.38073142980732994]
  Article systematically reviews over sixty methods for privacy-preserving NLP published between 2016 and 2020. We introduce a novel taxonomy for classifying the existing methods into three categories: methods trusted methods verification methods. We discuss open challenges in privacy-preserving NLP regarding data traceability, overhead dataset size and the prevalence of human biases in embeddings.
  arXiv  Detail & Related papers  (2022-05-20T11:29:44Z)
• Debugging Differential Privacy: A Case Study for Privacy Auditing [60.87570714269048]
  We show that auditing can also be used to find flaws in (purportedly) differentially private schemes. In this case study, we audit a recent open source implementation of a differentially private deep learning algorithm and find, with 99.99999999% confidence, that the implementation does not satisfy the claimed differential privacy guarantee.
  arXiv  Detail & Related papers  (2022-02-24T17:31:08Z)

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.