IR-LDP: Threshold-Driven Quality-Aware Incentives From a Public Bid-Bounding Perspective

Mobile crowdsensing (MCS), a human-in-the-loop IoT sensing paradigm, relies on active user participation. However, in untrusted environments, incentive design faces a fundamental trilemma among strict local differential privacy (LDP), individual rationality (IR), and system efficiency under limited budgets. LDP perturbation can obscure cost–quality correlations and undermine quality-aware selection, and it may even push selected users into negative utility. Meanwhile, most existing mechanisms either overlook privacy concerns or depend on heavyweight cryptographic frameworks, which are often impractical for resource-constrained mobile devices. To address these challenges, we propose IR-LDP, a lightweight LDP auction mechanism that explicitly manages the economic risk introduced by LDP noise under fixed budgets while guaranteeing deterministic IR. Specifically, IR-LDP introduces two design notions for LDP auctions: a public safety threshold that ensures ex-post IR for selected low-cost users, and a publicly bounded loss guarantee that caps each winner’s worst-case utility loss using only public system parameters. To realize these guarantees, IR-LDP employs a public projection strategy that projects noisy bids onto the safety threshold to bound downside utility risk, together with a nonlinear quality-aware ranking rule that prioritizes high-ROI users and mitigates the “lemon market” effect induced by LDP noise. We prove that IR-LDP satisfies ϵ-LDP for reported bids, maintains strict budget feasibility, and runs inO(NlogN) time. Extensive simulations across diverse budget regimes and user populations further demonstrate that IR-LDP outperforms representative baselines in social welfare, sensing quality, and budget efficiency, while providing ex-post IR for users with costs below the public threshold and a publicly known bound on utility loss for all participants.