Collecting and Analyzing Multidimensional Data with Local Differential Privacy

By Ning Wang, Xiaokui Xiao, Y. Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, Ge Yu. 2019

394 citations recorded by the source corpus.

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Statistics and Probability, Privacy & Security

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Citations in the corpus

Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2019, background): “The work in [27] proposed solutions to building up an LDP-compliant SGD, which powers a variety of important ML tasks.”
Federated Model Distillation with Noise-Free Differential Privacy (2020, methodology): “In order to protect training data privacy in FL, various privacy protection techniques have been proposed in the literature [Geyer et al., 2017; McMahan et al., 2018; Bonawitz et al., 2017; Wang et al., 2019b; Zhao et al., 2020; Sun et al., 2021; Lyu et al., 2020].”
Locally Differentially Private Data Collection and Analysis (2019, methodology): “In practice, the stochastic gradient descent (SGD) algorithm is often used to compute the target θ where we have the minimum (or hopefully) loss.”
LDP-Fed: federated learning with local differential privacy (2020, methodology): “Existing solutions to protect FL systems from such privacy attacks require trusted aggregators [17] or heavy cryptographic techniques [6, 23] which do not allow individual participants to define different local privacy guarantees, are insufficient for meaningfully protecting each high dimensional pa”
Distributed Clustering in the Anonymized Space with Local Differential Privacy (2019, background): “Based on differential privacy, the notion of LDP (Local Differential Privacy) is also proposed to protect local privacy context from data analysis [16], [17], [18], [19].”
Local Differential Privacy-Based Federated Learning for Internet of Things (2020, methodology): “We also follow the practice of [8], which combines different mechanisms Duchi and PM to obtain a hybrid mechanism HM.”