The convergence of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) is expected to enhance sensing coverage, connectivity, and resilience in distributed smart grids, especially in remote or infrastructure-sparse regions. In this UAV-assisted, IoT-enabled paradigm, UAVs act as aerial relays that collect, aggregate, and forward sensing data between ground devices and control centers. However, privacy-preserving data aggregation (PPDA) in such settings still faces key-escrow vulnerabilities, certificate management overhead, incomplete privacy protection, and high computational and energy costs, particularly for signature verification at UAV relays and decryption at control centers. To address these challenges, we propose LCE-PPDA, a lightweight, certificateless, and escrow-free PPDA scheme tailored for UAV-assisted, IoT-enabled smart grids. LCE-PPDA eliminates key escrow through joint key generation, adopts a hierarchical timing structure with macro-interval rekeying and micro-interval reporting, and supports ciphertext-level in-network aggregation with both individual and batch authentication at UAV relays. To ensure privacy with accountability, it integrates certificateless signatures, dynamic pseudonyms, and session-bound key masking, achieving end-to-end confidentiality, conditional anonymity, unlinkability, and accountable traceability. Formal analysis shows that LCE-PPDA achieves correctness and EUF-CMA security in the random-oracle model under the ECDLP assumption against both Type-I and Type-II adversaries. Performance evaluation further demonstrates that LCE-PPDA reduces computational, communication, and energy overheads compared with representative schemes, providing a scalable and lightweight foundation for secure, privacy-preserving data aggregation in UAV-assisted, IoT-enabled smart grids.

As the prevalence of high-speed networks with augmented transmission capacities expands, the security technologies devised to shield these networks lag behind the rapid advancements. This disparity leads to many issues, with Distributed Denial of Service (DDoS) attacks representing the most harmful. In such attacks, hackers exploit numerous bots to flood the target with a torrent of vicious traffic or spurious requests, leading to the paralysis of the communication framework and the interruption of essential services. Existing defense mechanisms against DDoS attacks struggle to reconcile detection speed, lead time, and accuracy, revealing several deficiencies. Our method begins by employing a sketch-based algorithm characterized by minimal computational demands paired with considerable swiftness. This strategy significantly mitigates the intensity of DDoS attacks and reduces the imbalance rate of benign traffic and attacks. Subsequently, the deployment of the CatBoost classifier augments the detection's F1-score to$9 9. 6 4 \%$. The experimental findings corroborate the efficacy of DDoS-Dam in the expeditious detection and mitigation of DDoS attacks in high-speed networks.