Local Differential Privacy for Person-to-Person Interactions

Sei, Yuichi; Ohsuga, Akihiko

doi:10.1109/ojcs.2022.3228999

IEEE Open J. Comput. Soc.

2022

DOI: 10.1109/ojcs.2022.3228999

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Local Differential Privacy for Person-to-Person Interactions

Yuichi Sei

Akihiko Ohsuga

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2024

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“…These challenges are particularly pronounced in the context of a smart city environment. The content of this chapter is grounded in the author's previous publications [ 52,54,55,60]. There are several other issues concerning LDP for smart cities.…”

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confidence: 99%

Privacy-Preserving Data Collection and Analysis for Smart Cities

Sei

2024

Human-Centered Services Computing for Smart Cities

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Smart cities leverage real-world data to digitally replicate city-related aspects such as disaster prevention, transportation, and pandemics, creating an encompassing virtual environment. The construction of this realistic virtual world necessitates the collection of individual behavioral data through Internet of Things (IoT) environments. However, the challenge lies in ensuring the privacy of individuals during this data collection process. While numerous studies exist on privacypreserving data mining, most target clean, complete, and independent personal data. This overlooks the reality of real-world personal data, which often contains noise, missing values, and evidence of interpersonal interactions. To build a human-centric smart city, it is crucial to consider these imperfect and interactive data while preserving privacy. In this paper, we propose a novel framework for privacy-preserving data collection and analysis in smart cities. This framework acknowledges the inherent sensing errors and interpersonal interactions, ensuring a more accurate representation of real-world conditions while maintaining stringent privacy safeguards. IntroductionComprehensive efforts have been made to establish an intelligent urban environment by developing sophisticated digital twins. Digital twins encapsulate the diverse functionalities of urban landscapes and accurately represent the behavioral patterns of their inhabitants. By creating a virtual counterpart to a city from data gathered in the real world, a myriad of urban-related events and processes, including disaster mitigation, transportation management, and pandemic response, are simulated within the digital realm. Subsequently, the insights gleaned from these simulations can be fed back into the physical world to facilitate well-informed decision-making and foster more sustainable urban development.

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confidence: 99%

Privacy-Preserving Data Collection and Analysis for Smart Cities

Sei

2024

Human-Centered Services Computing for Smart Cities

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Local Differential Privacy for Person-to-Person Interactions

Cited by 1 publication

References 29 publications

Privacy-Preserving Data Collection and Analysis for Smart Cities

Privacy-Preserving Data Collection and Analysis for Smart Cities

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