Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

• URL: http://arxiv.org/abs/2102.10802v1
• Date: Mon, 22 Feb 2021 06:58:46 GMT
• Title: Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval
• Authors: Praneeth Vepakomma, Julia Balla, Ramesh Raskar
• Abstract summary: We present a novel differentially private method textitPrivateMail for supervised manifold learning. We show extensive privacy-utility tradeoff results, as well as the computational efficiency and practicality of our methods.
• Score: 14.93584434176082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential Privacy offers strong guarantees such as immutable privacy under post processing. Thus it is often looked to as a solution to learning on scattered and isolated data. This work focuses on supervised manifold learning, a paradigm that can generate fine-tuned manifolds for a target use case. Our contributions are two fold. 1) We present a novel differentially private method \textit{PrivateMail} for supervised manifold learning, the first of its kind to our knowledge. 2) We provide a novel private geometric embedding scheme for our experimental use case. We experiment on private "content based image retrieval" - embedding and querying the nearest neighbors of images in a private manner - and show extensive privacy-utility tradeoff results, as well as the computational efficiency and practicality of our methods.

Related papers

• 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 Node-level Privacy in Graph Neural Networks [8.823710998526705]
  We propose a solution that addresses the issue of node-level privacy in Graph Neural Networks (GNNs) Our protocol consists of two main components: 1) a sampling routine called HeterPoisson, which employs a specialized node sampling strategy and a series of tailored operations to generate a batch of sub-graphs with desired properties, and 2) a randomization routine that utilizes symmetric Laplace noise instead of the commonly used Gaussian noise. Our protocol enables GNN learning with good performance, as demonstrated by experiments on five real-world datasets.
  arXiv  Detail & Related papers  (2023-11-12T16:21:29Z)
• Independent Distribution Regularization for Private Graph Embedding [55.24441467292359]
  Graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged. We propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term.
  arXiv  Detail & Related papers  (2023-08-16T13:32:43Z)
• Considerations on the Theory of Training Models with Differential Privacy [13.782477759025344]
  In federated learning collaborative learning takes place by a set of clients who each want to remain in control of how their local training data is used. Differential privacy is one method to limit privacy leakage.
  arXiv  Detail & Related papers  (2023-03-08T15:56:27Z)
• On Differentially Private Online Predictions [74.01773626153098]
  We introduce an interactive variant of joint differential privacy towards handling online processes. We demonstrate that it satisfies (suitable variants) of group privacy, composition, and post processing. We then study the cost of interactive joint privacy in the basic setting of online classification.
  arXiv  Detail & Related papers  (2023-02-27T19:18:01Z)
• Smooth Anonymity for Sparse Graphs [69.1048938123063]
  differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity.
  arXiv  Detail & Related papers  (2022-07-13T17:09:25Z)
• 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)
• Auditing Differentially Private Machine Learning: How Private is Private SGD? [16.812900569416062]
  We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis. We do so via novel data poisoning attacks, which we show correspond to realistic privacy attacks.
  arXiv  Detail & Related papers  (2020-06-13T20:00:18Z)
• InfoScrub: Towards Attribute Privacy by Targeted Obfuscation [77.49428268918703]
  We study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework. We find our approach generates obfuscated images faithful to the original input images, and additionally increase uncertainty by 6.2$times$ (or up to 0.85 bits) over the non-obfuscated counterparts.
  arXiv  Detail & Related papers  (2020-05-20T19:48:04Z)
• Differentially Private Generation of Small Images [0.0]
  We numerically measure the privacy-utility trade-off using parameters from $epsilon$-$delta$ differential privacy and the inception score. Our experiments uncover a saturated training regime where an increasing privacy budget adds little to the quality of generated images.
  arXiv  Detail & Related papers  (2020-05-02T10:37:46Z)

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.