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Characterizing and Optimizing Differentially-Private Techniques for High-Utility, Privacy-Preserving Internet-of-Vehicles

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HCI in Mobility, Transport, and Automotive Systems (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14048))

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Abstract

Recent developments of advanced Human-Vehicle Interactions rely on Internet-of-Vehicles (IoV), to achieve large-scale communications and synchronizations of data in practice. IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, concerns of data privacy are rising, since privacy leak possibly occurs when all vehicles collect, process and share personal statistics (e.g. driver’s heart rate, skin conductance and etc.). Therefore, it’s important to understand how to efficiently apply modern privacy-preserving techniques on IoV. In this work, we first present a comprehensive study to characterize modern privacy-preserving techniques for IoV, and then propose a Differential Privacy(DP) privacy-protection framework specialized for unique characteristics of IoV. Our characterization focuses on DP, a representative set of mathematically-guaranteed mechanisms for both privacy-preserving processing and sharing of sensitive data. It demystifies the tradeoffs of deploying DP techniques, in terms of service quality and privacy-preserving effects. The lessons learned from characterization reveal the importance of data utility in DP-protected IoV and motivate us to examine new opportunities. To better balance tradeoffs and improve service quality, we introduce HUT, for high-utility, batched queries under DP-protection on IoV. We quantitatively examine the benefits of HUT, and experimentally show that, in an IoV context, HUT can reduce information loss by 95.69% while enabling strong mathematically-guaranteed protection over sensitive data. Based on our characterization and optimizations, we identify key challenges and opportunities for future studies, to enable privacy-preserving IoV with low service quality degradation.

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Notes

  1. 1.

    11 dimensions include Facial Video, Vehicle Speed, Vehicle Acceleration, Vehicle Coordinate, Distance of Vehicle Ahead, Steering Wheel Coordinates, Throttle Status, Brake Status, Heart Rate, Skin Conductance, and Eye Tracking.

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Acknowledgements

We thank the anonymous reviewers from AutomotiveUI 2022 and HCI 2023 for their valuable feedback. We thank all members from User-Centric Computing Group for their valuable feedback and discussions. Earlier versions of this work was released at [9, 24].

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Authors and Affiliations

  1. User-Centric Computing Group, Ningbo, China

    Yicun Duan, Junyu Liu, Xiaoxing Ming, Wangkai Jin, Zilin Song & Xiangjun Peng

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  1. Yicun Duan
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Correspondence to Yicun Duan .

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  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

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Duan, Y., Liu, J., Ming, X., Jin, W., Song, Z., Peng, X. (2023). Characterizing and Optimizing Differentially-Private Techniques for High-Utility, Privacy-Preserving Internet-of-Vehicles. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2023. Lecture Notes in Computer Science, vol 14048. Springer, Cham. https://doi.org/10.1007/978-3-031-35678-0_3

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