Application and analysis of multidimensional negative surveys in participatory sensing applications

Groat, Michael M.; Edwards, Benjamin; Horey, James; He, Wenbo; Forrest, Stephanie

doi:10.1016/j.pmcj.2012.12.004

Cited by 17 publications

(2 citation statements)

References 48 publications

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“…We compared the performances of the proposed method and four state-of-the-art methods: O-RAPPOR [ 25], S2Mb [ 52], MDN [ 17], and PDE/ETE (the baseline approach).…”

Section: Evaluation Settingmentioning

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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“…We compared the performances of the proposed method and four state-of-the-art methods: O-RAPPOR [ 25], S2Mb [ 52], MDN [ 17], and PDE/ETE (the baseline approach).…”

Section: Evaluation Settingmentioning

confidence: 99%

Privacy-Preserving Data Collection and Analysis for Smart Cities

Sei

2024

Human-Centered Services Computing for Smart Cities

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“…Based on the knowledge of the above transformation matrix M , once the semi-honest cloud server obtains y 1 (or y 0 ), the number of the partners who provide the answer 'yes' (or 'no'), he/she would post-process this number to estimate x 1 (or x 0 ), the approximate number of the partners whose true answer are 'yes' (or 'no'). The estimation is an unbiased MLE (Maximum Likelihood Estimate) (Huang and Du 2008) and can be executed by the following equation (Groat et al 2013;Sei and Ohsuga 2017):…”

Section: Local Differential Privacymentioning

confidence: 99%

DPWeVote: differentially private weighted voting protocol for cloud-based decision-making

Yan

Liu

2018

Enterprise Information Systems

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With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express 'YES/NO' opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semihonest environment.

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Private rank aggregation under local differential privacy

Yan

Liu

2020

Int J Intell Syst

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In typical collective decision making scenarios, rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregated ranking that agrees the most with all the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. All existing works that guarantee differential privacy in rank aggregation assume that the data curator is trusted. In this paper, we first formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. We propose an effective and efficient protocol LDP-KwikSort, with the appealing property that each agent only needs to answer a small number of pairwise comparison queries from the untrusted curator with controllable noise, and the aggregated ranking could maintain an acceptable utility compared with that of the non-private protocol. Theoretical and empirical results demonstrate that the proposed solution can achieve the practical trade-off between the utility of aggregated ranking and the privacy preserving of agents' pairwise preferences.

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Application and analysis of multidimensional negative surveys in participatory sensing applications

Cited by 17 publications

References 48 publications

Privacy-Preserving Data Collection and Analysis for Smart Cities

Privacy-Preserving Data Collection and Analysis for Smart Cities

DPWeVote: differentially private weighted voting protocol for cloud-based decision-making

Private rank aggregation under local differential privacy

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