Privacy-aware incentive mechanism for mobile crowd sensing

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

“…In this case, the framework applies the K-means machine learning algorithm to cluster the trajectories from real-world GPS datasets and ensure the K-anonymity for high-sensitive datasets. Using the Kanonymity [128], [129], the framework can collect location information from K mobile users within a cloaking region, i.e., the region where the mobile users' exact locations are hidden [130], [131]. In [132], the use of K-anonymity is extended into a continuous network location privacy anonymity, i.e., KDT -anonymity, which not only considers the average anonymity size K, but also takes the average distance deviation D and the anonymity duration T into account.…”

“…In this case, the framework applies the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} -means machine learning algorithm to cluster the trajectories from real-world GPS datasets and ensure the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} -anonymity for high-sensitive datasets. Using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} -anonymity [127] , [128] , the framework can collect location information from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} mobile users within a cloaking region, i.e., the region where the mobile users’ exact locations are hidden [129] , [130] . In [131] , the use of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} -anonymity is extended into a continuous network location privacy anonymity, i.e., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$KDT$ \end{document} -anonymity, which not only considers the average anonymity size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} , but also takes the average distance deviation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$D$ \end{document} and the anonymity duration \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$T$ \end{document} into account.…”

An ADMM-based incentive approach for cooperative data analysis in edge computing

“…where the log function is used to model the diminishing returns on FD i 's data [14]. To obtain the resulting data with enough diversity [4], the following constraint should be satisfied…”

“…where µ i ∈ (0, 1] is the empirical parameter that statistically measures the data quality and the contribution of FD i [9,14], and C is the threshold given by the LD for measuring participant's contribution .…”

Incentive mechanism design for citizen reporting application using Stackelberg game

“…Previous work by [7] provided an efficient algorithm to compute the unique Stackelberg equilibrium at which the crowdsourcer utility is maximized. The extension of this work is provided by [8], [19] to include privacy considerations and spatial coverage of the collected dataset. However, the optimization of the crowdsourcer utility in these previous works is less relevant for the case when we want to optimize the profit of several regional working units simultaneously.…”