Providing Input-Discriminative Protection for Local Differential Privacy

Gu, Xiaolan; Li, Ming; Xiong, Li; Cao, Yang

doi:10.1109/icde48307.2020.00050

Cited by 43 publications

(52 citation statements)

References 17 publications

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“…Some existing work [ 57 , 203 ] aimed to propose personalized LDP-based frameworks for private histogram estimation. Gu et al [ 59 ] presented Input-Discriminative LDP (ID-LDP) that is a fine-grained privacy notion and reflects the distinct privacy requirements of different inputs. However, adopting personalized/granular privacy constraints still raises further concerns when considering complex system architectures and ensuring good data utility.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

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A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

“…However, Gu et al. [ 59 ] further indicated that different data have distinct sensitivity levels. Thus, they presented the Input-Discriminative LDP (ID-LDP) which is a more fine-grained version of LDP.…”

Section: Theoretical Summarization Of Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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“…Existing DP-based learning algorithms include local differential privacy (LDP) [20][21][22] and DP-based distributed SGD [23]. In LDP, each client perturbs its information locally and only sends a random version to the server, thereby protecting the client and server from the leakage of private information.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [21] proposed a solution to establish an SGD compliant with the LDP standard, which provides impetus for various important ML tasks. The work in [22] considered the distributed estimation of the data uploaded by the client by the server and used LDP to protect these data. The work in [23] improved the calculation efficiency of DP-based SGD by tracking the detailed information of privacy loss and obtains an accurate estimate of the overall privacy loss.…”

Section: Related Workmentioning

confidence: 99%

Federated Learning Model with Adaptive Differential Privacy Protection in Medical IoT

Huang

Wei

et al. 2021

Wireless Communications and Mobile Computing

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With the proliferation of intelligent services and applications authorized by artificial intelligence, the Internet of Things has penetrated into many aspects of our daily lives, and the medical field is no exception. The medical Internet of Things (MIoT) can be applied to wearable devices, remote diagnosis, mobile medical treatment, and remote monitoring. There is a large amount of medical information in the databases of various medical institutions. Nevertheless, due to the particularity of medical data, it is extremely related to personal privacy, and the data cannot be shared, resulting in data islands. Federated learning (FL), as a distributed collaborative artificial intelligence method, provides a solution. However, FL also involves multiple security and privacy issues. This paper proposes an adaptive Differential Privacy Federated Learning Medical IoT (DPFL-MIoT) model. Specifically, when the user updates the model locally, we propose a differential privacy federated learning deep neural network with adaptive gradient descent (DPFLAGD-DNN) algorithm, which can adaptively add noise to the model parameters according to the characteristics and gradient of the training data. Since privacy leaks often occur in downlink, we present differential privacy federated learning (DP-FL) algorithm where adaptive noise is added to the parameters when the server distributes the parameters. Our method effectively reduces the addition of unnecessary noise, and at the same time, the model has a good effect. Experimental results on real-world data show that our proposed algorithm can effectively protect data privacy.

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“…12a). Results have shown that IDUE(O) achieves higher accuracy than IDUE(R) [18]. However, the effect of the measured value being different from the true value is more significant in the peak dataset.…”

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confidence: 96%

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Sei

Ohsuga

2022

IEEE Access

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Available data may differ from true data in many cases due to sensing errors, especially for the Internet of Things (IoT). Although privacy-preserving data mining has been widely studied during the last decade, little attention has been paid to data values containing errors. Differential privacy, which is the de facto standard privacy metric, can be achieved by adding noise to a target value that must be protected. However, if the target value already contains errors, there is no reason to add extra noise. In this paper, a novel privacy model called true-value-based differential privacy (TDP) is proposed. This model applies traditional differential privacy to the "true value" unknown by the data owner or anonymizer but not to the "measured value" containing errors. Based on TDP, the amount of noise added by differential privacy techniques can be reduced by approximately 20% by our solution. As a result, the error of generated histograms can be reduced by 40.4% and 29.6% on average according to mean square error and Jensen-Shannon divergence, respectively.We validate this result on synthetic and five real data sets. Moreover, we proved that the privacy protection level does not decrease as long as the measurement error is not overestimated. INDEX TERMSData mining, Data privacy, Differentnial privacy, Internet of Things I. INTRODUCTION 1 Significant amounts of IoT data are generated every day by 2 many different sensors, such as thermal cameras, home appli-3 ance sensors, automotive sensors, and smartphone-equipped 4 sensors. These IoT data can be used for health monitoring [1], 5 context-aware recommendation (or recommender) systems 6 [2], navigation [3], and other applications.7 However, sensing people or their surrounding environment 8 might involve information that identifies an individual [4]. 9 Thus private information is at risk of leakage. By anonymiz-10 ing data based on ϵ-differential privacy [5], [6], which is the 11 de facto standard privacy metric (ϵ represents the privacy 12 budget), privacy leakage can be controlled. Differential pri-13 vacy has been used in many studies, such as [7]-[9], as it is 14 one of the most critical privacy metrics [10]. It is considered 15 an important concept for data analysis [11], [12]. 16 Local differential privacy is a specialized concept of dif-17 ferential privacy especially for data collection from each 18 person. In this paper, "differential privacy," refers to "lo-19

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Providing Input-Discriminative Protection for Local Differential Privacy

Cited by 43 publications

References 17 publications

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Federated Learning Model with Adaptive Differential Privacy Protection in Medical IoT

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

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