Continuous Release of Data Streams under both Centralized and Local Differential Privacy

• URL: http://arxiv.org/abs/2005.11753v2
• Date: Thu, 7 Dec 2023 20:02:17 GMT
• Title: Continuous Release of Data Streams under both Centralized and Local Differential Privacy
• Authors: Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, Somesh Jha
• Abstract summary: We study the problem of publishing a stream of real-valued data satisfying differential privacy (DP) One major challenge is that the maximal possible value can be quite large. We develop a method that uses the Exponential Mechanism with a quality function that approximates well the utility goal.
• Score: 30.998501044718548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be quite large; thus it is necessary to estimate a threshold so that numbers above it are truncated to reduce the amount of noise that is required to all the data. The estimation must be done based on the data in a private fashion. We develop such a method that uses the Exponential Mechanism with a quality function that approximates well the utility goal while maintaining a low sensitivity. Given the threshold, we then propose a novel online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps into a framework for private publishing of stream data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noises to the stream, and a smoother that improves the result using post-processing. Within our framework, we design an algorithm satisfying the more stringent setting of DP called local DP (LDP). To our knowledge, this is the first LDP algorithm for publishing streaming data. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of 6-10 orders of magnitude in terms of utility (measured by the mean squared error of answering a random range query).

Related papers

• Improving Noise Efficiency in Privacy-preserving Dataset Distillation [59.57846442477106]
  We introduce a novel framework that decouples sampling from optimization for better convergence and improves signal quality.<n>On CIFAR-10, our method achieves a textbf10.0% improvement with 50 images per class and textbf8.3% increase with just textbfone-fifth the distilled set size of previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-03T13:15:52Z)
• Benchmarking Fraud Detectors on Private Graph Data [70.4654745317714]
  Currently, many types of fraud are managed in part by automated detection algorithms that operate over graphs.<n>We consider the scenario where a data holder wishes to outsource development of fraud detectors to third parties.<n>Third parties submit their fraud detectors to the data holder, who evaluates these algorithms on a private dataset and then publicly communicates the results.<n>We propose a realistic privacy attack on this system that allows an adversary to de-anonymize individuals' data based only on the evaluation results.
  arXiv  Detail & Related papers  (2025-07-30T03:20:15Z)
• Dual Utilization of Perturbation for Stream Data Publication under Local Differential Privacy [10.07017446059039]
  Local differential privacy (LDP) has emerged as a promising standard. Applying LDP to stream data presents significant challenges, as stream data often involves a large or even infinite number of values. We introduce the Iterative Perturbation IPP method, which utilizes current perturbed results to calibrate the subsequent perturbation process. We prove that these three algorithms satisfy $w$-event differential privacy while significantly improving utility.
  arXiv  Detail & Related papers  (2025-04-21T09:51:18Z)
• Linear-Time User-Level DP-SCO via Robust Statistics [55.350093142673316]
  User-level differentially private convex optimization (DP-SCO) has garnered significant attention due to the importance of safeguarding user privacy in machine learning applications. Current methods, such as those based on differentially private gradient descent (DP-SGD), often struggle with high noise accumulation and suboptimal utility. We introduce a novel linear-time algorithm that leverages robust statistics, specifically the median and trimmed mean, to overcome these challenges.
  arXiv  Detail & Related papers  (2025-02-13T02:05:45Z)
• Value-Based Deep RL Scales Predictably [100.21834069400023]
  We show that value-based off-policy RL methods are predictable despite community lore regarding their pathological behavior. We validate our approach using three algorithms: SAC, BRO, and PQL on DeepMind Control, OpenAI gym, and IsaacGym.
  arXiv  Detail & Related papers  (2025-02-06T18:59:47Z)
• Differentially private and decentralized randomized power method [15.955127242261808]
  This paper proposes enhanced privacy-preserving variants of the randomized power method.<n>First, we propose a variant that reduces the amount of the noise required in current techniques to achieve Differential Privacy.<n>Second, we adapt our method to a decentralized framework in which data is distributed among multiple users.
  arXiv  Detail & Related papers  (2024-11-04T09:53:03Z)
• Noise Variance Optimization in Differential Privacy: A Game-Theoretic Approach Through Per-Instance Differential Privacy [7.264378254137811]
  Differential privacy (DP) can measure privacy loss by observing the changes in the distribution caused by the inclusion of individuals in the target dataset. DP has been prominent in safeguarding datasets in machine learning in industry giants like Apple and Google. We propose per-instance DP (pDP) as a constraint, measuring privacy loss for each data instance and optimizing noise tailored to individual instances.
  arXiv  Detail & Related papers  (2024-04-24T06:51:16Z)
• Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach [0.0]
  Federated learning (FL) develops global models without direct access to local datasets. DP offers a framework that gives a privacy guarantee by adding certain amounts of noise to parameters. We propose adaptive noise addition in FL which decides the value of injected noise based on features' relative importance.
  arXiv  Detail & Related papers  (2024-01-04T03:01:15Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization [94.06564567766475]
  DP-SGD and DP-NSGD mitigate the risk of large models memorizing sensitive training data. We show that these two algorithms achieve similar best accuracy while DP-NSGD is comparatively easier to tune than DP-SGD.
  arXiv  Detail & Related papers  (2022-06-27T03:45:02Z)
• Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates [0.0]
  We develop a DP inexact alternating direction method of multipliers algorithm with multiple local updates for federated learning. We show that our algorithm provides $barepsilon$-DP for every iteration, where $barepsilon$ is a privacy budget controlled by the user. We demonstrate that our algorithm reduces the testing error by at most $31%$ compared with the existing DP algorithm, while achieving the same level of data privacy.
  arXiv  Detail & Related papers  (2022-02-18T19:58:47Z)
• Scaling Structured Inference with Randomization [64.18063627155128]
  We propose a family of dynamic programming (RDP) randomized for scaling structured models to tens of thousands of latent states. Our method is widely applicable to classical DP-based inference. It is also compatible with automatic differentiation so can be integrated with neural networks seamlessly.
  arXiv  Detail & Related papers  (2021-12-07T11:26:41Z)
• SreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm [60.61943386819384]
  Existing implementations of KRR require that all the data is stored in the main memory. We propose StreaMRAK - a streaming version of KRR. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum.
  arXiv  Detail & Related papers  (2021-08-23T21:03:09Z)
• Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods [18.317488965846636]
  We study private synthetic data generation for query release. The goal is to construct a sanitized version of a sensitive dataset subject to differential privacy. Under this framework, we propose two new methods.
  arXiv  Detail & Related papers  (2021-06-14T04:19:35Z)
• Differentially Private Federated Learning via Inexact ADMM [0.0]
  Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks. We develop a DP inexact alternating direction method of multipliers algorithm that solves a sequence of trust-region subproblems. Our algorithm reduces the testing error by at most $22%$ compared with the existing DP algorithm, while achieving the same level of data privacy.
  arXiv  Detail & Related papers  (2021-06-11T02:28:07Z)
• Privacy Preserving Recalibration under Domain Shift [119.21243107946555]
  We introduce a framework that abstracts out the properties of recalibration problems under differential privacy constraints. We also design a novel recalibration algorithm, accuracy temperature scaling, that outperforms prior work on private datasets.
  arXiv  Detail & Related papers  (2020-08-21T18:43:37Z)
• User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization [77.43075255745389]
  Federated learning (FL) is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. From a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. We propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers.
  arXiv  Detail & Related papers  (2020-02-29T10:13:39Z)

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.