Local Differential Privacy for Data Streams

Zeng, Qingkui; Yang, Gaoming

doi:10.1007/978-981-15-9129-7_11

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…namely Laplace mechanism for numerical data and exponential mechanism for non-numerical one [26]. For example, Yang et al (2020) [27] proposed a privacy-preserving framework for student health data on smart wearable devices, improving the centered differential privacy technique by adding noise with a Laplacian mechanism and filtering the appropriate data for publication through shielding conditions. It reduces the possibility of an attacker finding abnormal data so as to infer the user's privacy information.…”

Section: Plos Onementioning

confidence: 99%

“…Errounda et al [10] applied w-event privacy mechanism for location statistics based on local differential privacy and used an approximation strategy to estimate the unperturbed locations, but similarly may incur unnecessary utility loss. Fang et al [11] proposed local differential Private Streaming Protocol (LDPS), based on local differential privacy with w-event privacy mechanism. It can adaptively invoke methods to handle categorical and numerical data, but the resource consumption is too large to be applicable to wearable devices whose resources are limited.…”

Section: Plos Onementioning

confidence: 99%

“…Errounda et al (2021) [10] applied w-event privacy mechanism on location statistics, but with the same unnecessary utility loss. Fang et al (2020) [11] also based on this mechanism and proposed Local Differential Privacy Data Streaming Protocol (LDPS), adaptively invoked method to handle categorical and numerical data. However, the resource consumption is too large and not suitable for wearable devices.…”

Section: Introductionmentioning

confidence: 99%

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Local differential privacy protection for wearable device data

Wang

et al. 2022

PLoS ONE

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Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users’ personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local differential privacy protection for wearable device data

Wang

et al. 2022

PLoS ONE

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“…Also, the method does not account for the privacy loss for drift detection and the privacy loss of updates when no concept drift is detected. The works in [14,18,19,26,27] focused on releasing summary statistics such as counts, mean, mode, range queries, centroids, etc.…”

Section: Related Workmentioning

confidence: 99%

Differentially Private Ensemble Classifiers for Data Streams

Gondara

Wang

Carvalho

2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Learning from continuous data streams via classification/regression is prevalent in many domains. Adapting to evolving data characteristics (concept drift) while protecting data owners' private information is an open challenge. We present a differentially private ensemble solution to this problem with two distinguishing features: it allows an unbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it is model agnostic, in that it treats any pre-trained differentially private classification/regression model as a black-box. Our method outperforms competitors on real-world and simulated datasets for varying settings of privacy, concept drift, and data distribution. CCS CONCEPTS• Security and privacy → Privacy-preserving protocols; • Information systems → Data stream mining.

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“…Hassan et al [10] propose instantaneous data reporting with peak value preservation using Laplace noise. Fang et al [28] propose local differential private streaming (LDPS) protocol for numerical and categorical attributes. LDPS satisfy local differential privacy and sliding window-based w-event privacy.…”

Section: Related Workmentioning

confidence: 99%

Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Afrose

Yao

Kotevska

2021

2021 18th International Conference on Privacy, Security and Trust (PST)

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Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user's sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan).

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Local Differential Privacy for Data Streams

Cited by 8 publications

References 19 publications

Local differential privacy protection for wearable device data

Local differential privacy protection for wearable device data

Differentially Private Ensemble Classifiers for Data Streams

Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

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