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In smart grid, the smart meters improve the grids’ efficiency but imply the sensitive residential information. Hence, how to prevent privacy leakage of smart meter data has drawn lots of researchers’ attentions. Yet, it is non‐trivial to quantify the relation between privacy protection behaviours and system utility loss. To this end, the authors leverage the notion of differential privacy (DP) to measure the privacy‐protection strength, under the framework of optimal power flow (OPF). Specifically, once the noise is injected to hide the actual demand, the solutions of OPF problem are probably affected, which undermine the grid utility. In this study, the authors are the first quantitatively investigating DP preserving OPF problem. Starting with re‐modelling the noise‐injected OPF problem, the authors rigorously prove OPF solution's sensitivity with respect to the uncertainty of demand. Moreover, aiming at OPF‐based pricing mechanism, locational marginal pricing (LMP), the respective privacy‐protection's contribution on LMPs is explicitly expressed. Subsequently, based on the extensive experiments, it is illustrated that the quantitative correlation between the privacy‐protection strength and the gird system performance. Furthermore, by combining the grid topology and privacy‐protection strength, a novel billing system to fairly charge the extra payment to subsidise the privacy‐insensitive customers is proposed.

Low-duty-cycle network plays an crucial role in improving energy efficiency of wireless communication, where nodes stay asleep most of time. Despite energy saving, the security of low-duty-cycle networks is of great concern. The attacking strategy design becomes even more challenging considering the stochastic transmission patterns arising from both the clock drift and other uncertainties. In this paper, we propose LearJam, a novel two-phase energy-efficient learningbased jamming attack strategy against low-duty-cycle networks, where the attacker estimates the distribution of transmission period in the learning phase, and schedules its jamming attacks in the attacking phase based on this estimated distribution. We jointly optimize the learning duration and the attacking duration under the energy constraint in order to degrade the network throughput to the maximal degree. We propose simple yet effective methods to solve both the single-node and multi-node scenarios. We further discuss a state-of-theart mechanism defending against LearJam by re-scheduling transmission pattern, which will aid the researchers to improve the security of low-duty-cycle networks. Extensive simulations show that our design achieves significantly higher number of successful attacks (increasing 38%-762%) in a sparse lowduty-cycle network compared with some traditional jamming strategies.