Abstract
As an enabling technology for edge computing scenarios, indoor localization has a broad prospect in a variety of location-based applications, such as tracking, navigating, and monitoring in indoor environments. In order to improve the location accuracy, numerous machine learning (ML)-based indoor localization schemes with fingerprint fusion have been proposed recently, which take advantage of the fusion of signal gathered from multiple wireless technologies (e.g., WiFi and BLE) and require a site survey to construct the fingerprint database. However, most solutions are based on cloud framework and thus pose a serious privacy leakage because users’ sensitive information (e.g., locations) is computed from the fingerprint database by the untrusted localization service provider. Furthermore, the site survey is time-consuming and labor-intensive. In this paper, we propose a differentially private fingerprint fusion semi-supervised extreme learning machine for indoor localization in the edge computing, called Adp-FSELM. The Adp-FSELM firstly employs a multi-level edge network-based privacy-preserving system framework to meet the requirements of ML-based fingerprint indoor localization for lightweight, low latency, and real-time response. Then, the Adp-FSELM extends the varepsilon-differential privacy to the fingerprint fusion semi-supervised extreme learning machine for indoor localization in edge computing through a three-phase private process consisting of private labeled sample obfuscation, differentially private feature fusion, and differentially private model training. Theoretical and comprehensive experimental results in real indoor environments demonstrate that the Adp-FSELM provides a high varepsilon-differential privacy guarantee for users’ location privacy while reducing human calibration effort and effectively resists Bayesian inference attacks. Compared with the existing semi-supervised learning-based localization methods, the mean absolute error of location accuracy of the Adp-FSELM is restricted to 2.22% at most, and the additional time consumption can be almost ignored. Thus, our mechanism can balance the trade-off among location privacy, location accuracy, and time consumption.
Highlights
The proliferation of Internet of everything (IoE) and sensor-rich smart IoE devices has spawned a wide range of indoor location-based services (ILS), such as indoor guidance and navigation, smart inventory, context-aware location-based marketing, personnel care and elderly care, disaster management and assist, and so forth
The results show that, compared with five semi-supervised learning methods on location accuracy and six mature localization methods on time consumption, the Adp-FSELM achieves the high accuracy with low time consumption while protecting location privacy
We focus on privacy-preserving problem of machine learning (ML)-based multi-fingerprints fusion indoor positioning in edge computing scenarios
Summary
The proliferation of Internet of everything (IoE) and sensor-rich smart IoE devices has spawned a wide range of indoor location-based services (ILS), such as indoor guidance and navigation, smart inventory, context-aware location-based marketing, personnel care and elderly care, disaster management and assist, and so forth. The nature characteristics of edge computing such as low latency, location awareness, distribution, programmability, and data localization just meet the requirements of big data processing, positioning delay, and accuracy improvement brought by cloud-based fingerprint IPS that utilizes ML algorithms. We employ a small set of labeled RSSI samples as well as a large number of unlabeled RSSI samples fused with the WiFi and BLE fingerprints to improve the positioning performance of the AdpFSELM with little manual calibration effort In this way, comprehensive experimental results demonstrate that, compared to five semi-supervised learning methods on location accuracy and six non-private localization methods on time consumption, the Adp-FSELM can provide the provably privacy-preserving with little resource cost and performance sacrifice.
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